Initial commit

fbshipit-source-id: d6798bb3ead07e6e3da5edebc53b946e6cfa0807

.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Shared objects
+*.so
+
+# Distribution / packaging
+lib/build/
+*.egg-info/
+*.egg
+
+# Temporary files
+*.swn
+*.swo
+*.swp
+
+# Dataset symlinks
+lib/datasets/data/*
+!lib/datasets/data/README.md
+
+# Generated C files
+lib/utils/cython_*.c

CONTRIBUTING.md

+# Contributing to Detectron
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Our Development Process
+Minor changes and improvements will be released on an ongoing basis. Larger
+changes (e.g., changesets implementing a new paper) will be released on a more
+periodic basis.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `master`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. Ensure no regressions in baseline model speed and accuracy.
+7. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Facebook's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+GitHub issues will be largely unattended and are mainly intended as a community
+forum for collectively debugging issues, hopefully leading to pull requests with
+fixes when appropriate.
+
+## Coding Style  
+* 4 spaces for indentation rather than tabs
+* 80 character line length
+* PEP8 formatting
+
+## License
+By contributing to Detectron, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

FAQ.md

+# FAQ
+
+This document covers frequently asked questions.
+
+- For general information about Detectron, please see [`README.md`](README.md).
+- For installation instructions, please see [`INSTALL.md`](INSTALL.md).
+- For a quick getting started guide, please see [`GETTING_STARTED.md`](GETTING_STARTED.md).
+
+#### Q: How do I compute validation AP during training?
+
+**A:** Detectron does not compute validation statistics (e.g., AP) during training because this slows training. Instead, we've implemented a "validation monitor", which is a process that polls for new model checkpoints saved by a training job and when one is found performs inference with it by scheduling a job with `tools/test_net.py` asynchronously using free GPUs in our cluster. We have not released the validation monitor because (1) it's a relatively thin wrapper on top of `tools/train_net.py` and (2) the little code that comprises it is specific to our cluster and would not be generally useful.
+
+#### Q: How do I restrict Detectron to use only a subset of the GPUs on a server?
+
+**A:** Don't modify the code; use the [`CUDA_VISIBLE_DEVICES`](http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) environment variable instead.
+
+#### Q: Detection on one image is really slow compared to the reported performance, why?
+
+A: Various algorithms and caches (e.g., from `cudnn`) take some time to warm up. Peak inference performance will not be reached until after a few images have been processed.
+
+Also potentially relevant: inference with Mask R-CNN on high-resolution images may be slow simply because substantial time is spent upsampling the predicted masks to the original image resolution (this has not been optimized). You can diagnose this issue if the `misc_mask` time reported by `tools/infer_simple.py` is high (e.g., much more than 20-90ms). The solution is to first resize your images such that the short side is around 600-800px (the exact choice does not matter) and then run inference on the resized image.
+
+
+#### Q: How do I implement a custom Caffe2 CPU or GPU operator for use in Detectron?
+
+**A:** Detectron uses a number of specialized Caffe2 operators that are distributed via the [Caffe2 Detectron module](https://github.com/caffe2/caffe2/tree/master/modules/detectron) as part of the core Caffe2 GitHub repository. If you'd like to implement a custom Caffe2 operator for your project, we have written a toy example illustrating how to add an operator under the Detectron source tree; please see [`lib/ops/zero_even_op.*`](lib/ops/) and [`tests/test_zero_even_op.py`](tests/test_zero_even_op.py). For more background on writing Caffe2 operators please consult the [Caffe2 documentation](https://caffe2.ai/docs/custom-operators.html).
+
+#### Q: How do I use Detectron to train a model on a custom dataset?
+
+**A:** If possible, we strongly recommend that you first convert the custom dataset annotation format to the [COCO API json format](http://cocodataset.org/#download). Then, add your dataset to the [dataset catalog](lib/datasets/dataset_catalog.py) so that Detectron can use it for training and inference. If your dataset cannot be converted to the COCO API json format, then it's likely that more significant code modifications will be required. If the dataset you're adding is popular, please consider making the converted annotations publicly available; If code modifications are required, please consider submitting a pull request.

GETTING_STARTED.md

+# Using Detectron
+
+This document provides brief tutorials covering Detectron for inference and training on the COCO dataset.
+
+- For general information about Detectron, please see [`README.md`](README.md).
+- For installation instructions, please see [`INSTALL.md`](INSTALL.md).
+
+## Inference with Pretrained Models
+
+#### 1. Directory of Image Files
+To run inference on a directory of image files (`demo/*.jpg` in this example), you can use the `infer_simple.py` tool. In this example, we're using an end-to-end trained Mask R-CNN model with a ResNet-101-FPN backbone from the model zoo:
+
+```
+python2 tools/infer_simple.py \
+    --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml \
+    --output-dir /tmp/detectron-visualizations \
+    --image-ext jpg \
+    --wts https://s3-us-west-2.amazonaws.com/detectron/35861858/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml.02_32_51.SgT4y1cO/output/train/coco_2014_train:coco_2014_valminusminival/generalized_rcnn/model_final.pkl \
+    demo
+```
+
+Detectron should automatically download the model from the URL specified by the `--wts` argument. This tool will output visualizations of the detections in PDF format in the directory specified by `--output-dir`. Here's an example of the output you should expect to see (for copyright information about the demo images see [`demo/NOTICE`](demo/NOTICE)).
+
+<div align="center">
+  <img src="demo/output/17790319373_bd19b24cfc_k_example_output.jpg" width="700px" />
+  <p>Example Mask R-CNN output.</p>
+</div>
+
+**Notes:**
+
+- When running inference on your own high-resolution images, Mask R-CNN may be slow simply because substantial time is spent upsampling the predicted masks to the original image resolution (this has not been optimized). You can diagnose this issue if the `misc_mask` time reported by `tools/infer_simple.py` is high (e.g., much more than 20-90ms). The solution is to first resize your images such that the short side is around 600-800px (the exact choice does not matter) and then run inference on the resized image.
+
+
+#### 2. COCO Dataset
+This example shows how to run an end-to-end trained Mask R-CNN model from the model zoo using a single GPU for inference. As configured, this will run inference on all images in `coco_2014_minival` (which must be properly installed).
+
+```
+python2 tools/test_net.py \
+    --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml \
+    TEST.WEIGHTS https://s3-us-west-2.amazonaws.com/detectron/35861858/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml.02_32_51.SgT4y1cO/output/train/coco_2014_train:coco_2014_valminusminival/generalized_rcnn/model_final.pkl \
+    NUM_GPUS 1
+```
+
+Running inference with the same model using `$N` GPUs (e.g., `N=8`).
+
+```
+python2 tools/test_net.py \
+    --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml \
+    --multi-gpu-testing \
+    TEST.WEIGHTS https://s3-us-west-2.amazonaws.com/detectron/35861858/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml.02_32_51.SgT4y1cO/output/train/coco_2014_train:coco_2014_valminusminival/generalized_rcnn/model_final.pkl \
+    NUM_GPUS $N
+```
+
+On an NVIDIA Tesla P100 GPU, inference should take about 130-140 ms per image for this example.
+
+
+## Training a Model with Detectron
+
+This is a tiny tutorial showing how to train a model on COCO. The model will be an end-to-end trained Faster R-CNN using a ResNet-50-FPN backbone. For the purpose of this tutorial, we'll use a short training schedule and a small input image size so that training and inference will be relatively fast. As a result, the box AP on COCO will be relatively low compared to our [baselines](MODEL_ZOO.md). This example is provided for instructive purposes only (i.e., not for comparing against publications).
+
+#### 1. Training with 1 GPU
+
+```
+python2 tools/train_net.py \
+    --cfg configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml \
+    OUTPUT_DIR /tmp/detectron-output
+```
+
+**Expected results:**
+
+- Output (models, validation set detections, etc.) will be saved under `/tmp/detectron-output`
+- On a Maxwell generation GPU (e.g., M40), training should take around 4.2 hours
+- Inference time should be around 80ms / image (also on an M40)
+- Box AP on `coco_2014_minival` should be around 22.1% (+/- 0.1% stdev measured over 3 runs)
+
+### 2. Multi-GPU Training
+
+We've also provided configs to illustrate training with 2, 4, and 8 GPUs using learning schedules that will be approximately equivalent to the one used with 1 GPU above. The configs are located at: `configs/getting_started/tutorial_{2,4,8}gpu_e2e_faster_rcnn_R-50-FPN.yaml`. For example, launching a training job with 2 GPUs will look like this:
+
+```
+python2 tools/train_net.py \
+    --multi-gpu-testing \
+    --cfg configs/getting_started/tutorial_2gpu_e2e_faster_rcnn_R-50-FPN.yaml \
+    OUTPUT_DIR /tmp/detectron-output
+```
+
+Note that we've also added the `--multi-gpu-testing` flag to instruct Detectron to parallelize inference over multiple GPUs (2 in this example; see `NUM_GPUS` in the config file) after training has finished.
+
+**Expected results:**
+
+- Training should take around 2.3 hours (2 x M40)
+- Inference time should be around 80ms / image (but in parallel on 2 GPUs, so half the total time)
+- Box AP on `coco_2014_minival` should be around 22.1% (+/- 0.1% stdev measured over 3 runs)
+
+To understand how learning schedules are adjusted (the "linear scaling rule"), please study these tutorial config files and read our paper [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677). **Aside from this tutorial, all of our released configs make use of 8 GPUs. If you will be using fewer than 8 GPUs for training (or do anything else that changes the minibatch size), it is essential that you understand how to manipulate training schedules according to the linear scaling rule.**
+
+**Notes:**
+
+- This training example uses a relatively low GPU-compute model and thus overhead from Caffe2 Python ops is relatively high. As a result, scaling as the number of GPUs is increased from 2 to 8 is relatively poor (e.g., training with 8 GPUs takes about 0.9 hours, only 4.5x faster than with 1 GPU). As larger, more GPU-compute heavy models are used, the scaling improves.

INSTALL.md

+# Installing Detectron
+
+This document covers how to install Detectron, its dependencies (including Caffe2), and the COCO dataset.
+
+- For general information about Detectron, please see [`README.md`](README.md).
+
+**Requirements:**
+
+- NVIDIA GPU, Linux, Python2
+- Caffe2, various standard Python packages, and the COCO API; Instructions for installing these dependencies are found below
+
+**Notes:**
+
+- Detectron operators currently do not have CPU implementation; a GPU system is required.
+- Detectron has been tested extensively with CUDA 8.0 and cuDNN 6.0.21.
+
+## Caffe2
+
+To install Caffe2 with CUDA support, follow the [installation instructions](https://caffe2.ai/docs/getting-started.html) from the [Caffe2 website](https://caffe2.ai/). **If you already have Caffe2 installed, make sure to update your Caffe2 to a version that includes the [Detectron module](https://github.com/caffe2/caffe2/tree/master/modules/detectron).**
+
+Please ensure that your Caffe2 installation was successful before proceeding by running the following commands and checking their output as directed in the comments.
+
+```
+# To check if Caffe2 build was successful
+python2 -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"
+
+# To check if Caffe2 GPU build was successful
+# This must print a number > 0 in order to use Detectron
+python2 -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())'
+```
+
+If the `caffe2` Python package is not found, you likely need to adjust your `PYTHONPATH` environment variable to include its location (`/path/to/caffe2/build`, where `build` is the Caffe2 CMake build directory).
+
+## Other Dependencies
+
+Install Python dependencies:
+
+```
+pip install numpy pyyaml matplotlib opencv-python>=3.0 setuptools Cython mock
+```
+
+Install the [COCO API](https://github.com/cocodataset/cocoapi):
+
+```
+# COCOAPI=/path/to/clone/cocoapi
+git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
+cd $COCOAPI/PythonAPI
+# Install into global site-packages
+make install
+# Alternatively, if you do not have permissions or prefer
+# not to install the COCO API into global site-packages
+python2 setup.py install --user
+```
+
+Note that instructions like `# COCOAPI=/path/to/install/cocoapi` indicate that you should pick a path where you'd like to have the software cloned and then set an environment variable (`COCOAPI` in this case) accordingly.
+
+## Detectron
+
+Clone the Detectron repository:
+
+```
+# DETECTRON=/path/to/clone/detectron
+git clone https://github.com/facebookresearch/detectron $DETECTRON
+```
+
+Set up Python modules:
+
+```
+cd $DETECTRON/lib && make
+```
+
+Check that Detectron tests pass (e.g. for [`SpatialNarrowAsOp test`](tests/test_spatial_narrow_as_op.py)):
+
+```
+python2 $DETECTRON/tests/test_spatial_narrow_as_op.py
+```
+
+## That's All You Need for Inference
+
+At this point, you can run inference using pretrained Detectron models. Take a look at our [inference tutorial](GETTING_STARTED.md) for an example. If you want to train models on the COCO dataset, then please continue with the installation instructions.
+
+## Datasets
+
+Detectron finds datasets via symlinks from `lib/datasets/data` to the actual locations where the dataset images and annotations are stored. For instructions on how to create symlinks for COCO and other datasets, please see [`lib/datasets/data/README.md`](lib/datasets/data/README.md).
+
+After symlinks have been created, that's all you need to start training models.
+
+## Advanced Topic: Custom Operators for New Research Projects
+
+Please read the custom operators section of the [`FAQ`](FAQ.md) first.
+
+For convenience, we provide CMake support for building custom operators. All custom operators are built into a single library that can be loaded dynamically from Python.
+Place your custom operator implementation under [`lib/ops/`](lib/ops/) and see [`tests/test_zero_even_op.py`](tests/test_zero_even_op.py) for an example of how to load custom operators from Python.
+
+Build the custom operators library:
+
+```
+cd $DETECTRON/lib && make ops
+```
+
+Check that the custom operator tests pass:
+
+```
+python2 $DETECTRON/tests/test_zero_even_op.py
+```
+
+## Docker Image
+
+We provide a [`Dockerfile`](docker/Dockerfile) that you can use to build a Detectron image on top of a Caffe2 image that satisfies the requirements outlined at the top. If you're using a prebuilt Caffe2 image (e.g. from the [docker repo](https://hub.docker.com/r/caffe2ai/caffe2/)), please make sure that it includes the [Detectron module](https://github.com/caffe2/caffe2/tree/master/modules/detectron). We also provide an example of how to build an up-to-date Caffe2 image.
+
+Build a Caffe2 image:
+
+```
+cd /path/to/caffe2/docker/ubuntu-16.04-cuda8-cudnn6-all-options
+# Use the latest Caffe2 master
+sed -i -e 's/ --branch v0.8.1//g' Dockerfile
+docker build -t caffe2:cuda8-cudnn6-all-options .
+```
+
+Build a Detectron image:
+
+```
+cd $DETECTRON/docker
+docker build -t detectron:c2-cuda8-cudnn6 .
+```
+
+Run the Detectron image (e.g. for [`BatchPermutationOp test`](tests/test_batch_permutation_op.py)):
+
+```
+nvidia-docker run --rm -it detectron:c2-cuda8-cudnn6 python2 tests/test_batch_permutation_op.py
+```
+
+## Troubleshooting
+
+In case of Caffe2 installation problems, please read the troubleshooting section of the relevant Caffe2 [installation instructions](https://caffe2.ai/docs/getting-started.html) first. In the following, we provide additional troubleshooting tips for Caffe2 and Detectron.
+
+### Caffe2 Operator Profiling
+
+Caffe2 comes with performance [`profiling`](https://github.com/caffe2/caffe2/tree/master/caffe2/contrib/prof)
+support which you may find useful for benchmarking or debugging your operators
+(see [`BatchPermutationOp test`](tests/test_batch_permutation_op.py) for example usage).
+Profiling support is not built by default and you can enable it by setting
+the `-DUSE_PROF=ON` flag when running Caffe2 CMake.
+
+### CMake Cannot Find CUDA and cuDNN
+
+Sometimes CMake has trouble with finding CUDA and cuDNN dirs on your machine.
+
+When building Caffe2, you can point CMake to CUDA and cuDNN dirs by running:
+
+```
+cmake .. \
+  # insert your Caffe2 CMake flags here
+  -DCUDA_TOOLKIT_ROOT_DIR=/path/to/cuda/toolkit/dir \
+  -DCUDNN_ROOT_DIR=/path/to/cudnn/root/dir
+```
+
+Similarly, when building custom Detectron operators you can use:
+
+```
+cd $DETECTRON/lib
+mkdir -p build && cd build
+cmake .. \
+  -DCUDA_TOOLKIT_ROOT_DIR=/path/to/cuda/toolkit/dir \
+  -DCUDNN_ROOT_DIR=/path/to/cudnn/root/dir
+make
+```
+
+Note that you can use the same commands to get CMake to use specific versions of CUDA and cuDNN out of possibly multiple versions installed on your machine.
+
+### Protobuf Errors
+
+Caffe2 uses protobuf as its serialization format and requires version `3.2.0` or newer.
+If your protobuf version is older, you can build protobuf from Caffe2 protobuf submodule and use that version instead.
+
+To build Caffe2 protobuf submodule:
+
+```
+# CAFFE2=/path/to/caffe2
+cd $CAFFE2/third_party/protobuf/cmake
+mkdir -p build && cd build
+cmake .. \
+  -DCMAKE_INSTALL_PREFIX=$HOME/c2_tp_protobuf \
+  -Dprotobuf_BUILD_TESTS=OFF \
+  -DCMAKE_CXX_FLAGS="-fPIC"
+make install
+```
+
+To point Caffe2 CMake to the newly built protobuf:
+
+```
+cmake .. \
+  # insert your Caffe2 CMake flags here
+  -DPROTOBUF_PROTOC_EXECUTABLE=$HOME/c2_tp_protobuf/bin/protoc \
+  -DPROTOBUF_INCLUDE_DIR=$HOME/c2_tp_protobuf/include \
+  -DPROTOBUF_LIBRARY=$HOME/c2_tp_protobuf/lib64/libprotobuf.a
+```
+
+You may also experience problems with protobuf if you have both system and anaconda packages installed.
+This could lead to problems as the versions could be mixed at compile time or at runtime.
+This issue can also be overcome by following the commands from above.
+
+### Caffe2 Python Binaries
+
+In case you experience issues with CMake being unable to find the required Python paths when
+building Caffe2 Python binaries (e.g. in virtualenv), you can try pointing Caffe2 CMake to python
+library and include dir by using:
+
+```
+cmake .. \
+  # insert your Caffe2 CMake flags here
+  -DPYTHON_LIBRARY=$(python2 -c "from distutils import sysconfig; print(sysconfig.get_python_lib())") \
+  -DPYTHON_INCLUDE_DIR=$(python2 -c "from distutils import sysconfig; print(sysconfig.get_python_inc())")
+```
+
+### Caffe2 with NNPACK Build
+
+Detectron does not require Caffe2 built with NNPACK support. If you face NNPACK related issues during Caffe2 installation, you can safely disable NNPACK by setting the `-DUSE_NNPACK=OFF` CMake flag.
+
+### Caffe2 with OpenCV Build
+
+Analogously to the NNPACK case above, you can disable OpenCV by setting the `-DUSE_OPENCV=OFF` CMake flag.
+
+### COCO API Undefined Symbol Error
+
+If you encounter a COCO API import error due to an undefined symbol, as reported [here](https://github.com/cocodataset/cocoapi/issues/35),
+make sure that your python versions are not getting mixed. For instance, this issue may arise if you have
+[both system and conda numpy installed](https://stackoverflow.com/questions/36190757/numpy-undefined-symbol-pyfpe-jbuf).
+
+### CMake Cannot Find Caffe2
+
+In case you experience issues with CMake being unable to find the Caffe2 package when building custom operators,
+make sure you have run `make install` as part of your Caffe2 installation process.

NOTICE

+Portions of this software are derived from py-faster-rcnn.
+
+==============================================================================
+py-faster-rcnn licence
+==============================================================================
+
+Faster R-CNN
+
+The MIT License (MIT)
+
+Copyright (c) 2015 Microsoft Corporation
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

README.md

+# Detectron
+
+Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including [Mask R-CNN](https://arxiv.org/abs/1703.06870). It is written in Python and powered by the [Caffe2](https://github.com/caffe2/caffe2) deep learning framework.
+
+At FAIR, Detectron has enabled numerous research projects, including: [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144), [Mask R-CNN](https://arxiv.org/abs/1703.06870), [Detecting and Recognizing Human-Object Interactions](https://arxiv.org/abs/1704.07333), [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002), [Non-local Neural Networks](https://arxiv.org/abs/1711.07971), [Learning to Segment Every Thing](https://arxiv.org/abs/1711.10370), and [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/abs/1712.04440).
+
+<div align="center">
+  <img src="demo/output/33823288584_1d21cf0a26_k_example_output.jpg" width="700px" />
+  <p>Example Mask R-CNN output.</p>
+</div>
+
+## Introduction
+
+The goal of Detectron is to provide a high-quality, high-performance
+codebase for object detection *research*. It is designed to be flexible in order
+to support rapid implementation and evaluation of novel research. Detectron
+includes implementations of the following object detection algorithms:
+
+- [Mask R-CNN](https://arxiv.org/abs/1703.06870) -- *Marr Prize at ICCV 2017*
+- [RetinaNet](https://arxiv.org/abs/1708.02002) -- *Best Student Paper Award at ICCV 2017*
+- [Faster R-CNN](https://arxiv.org/abs/1506.01497)
+- [RPN](https://arxiv.org/abs/1506.01497)
+- [Fast R-CNN](https://arxiv.org/abs/1504.08083)
+- [R-FCN](https://arxiv.org/abs/1605.06409)
+
+using the following backbone network architectures:
+
+- [ResNeXt{50,101,152}](https://arxiv.org/abs/1611.05431)
+- [ResNet{50,101,152}](https://arxiv.org/abs/1512.03385)
+- [Feature Pyramid Networks](https://arxiv.org/abs/1612.03144) (with ResNet/ResNeXt)
+- [VGG16](https://arxiv.org/abs/1409.1556)
+
+Additional backbone architectures may be easily implemented. For more details about these models, please see [References](#references) below.
+
+## License
+
+Detectron is released under the [Apache 2.0 license](https://github.com/facebookresearch/detectron/blob/master/LICENSE). See the [NOTICE](https://github.com/facebookresearch/detectron/blob/master/NOTICE) file for additional details.
+
+## Citing Detectron
+
+If you use Detectron in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
+
+```
+@misc{Detectron2018,
+  author =       {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and
+                  Piotr Doll\'{a}r and Kaiming He},
+  title =        {Detectron},
+  howpublished = {\url{https://github.com/facebookresearch/detectron}},
+  year =         {2018}
+}
+```
+
+## Model Zoo and Baselines
+
+We provide a large set of baseline results and trained models available for download in the [Detectron Model Zoo](MODEL_ZOO.md).
+
+## Installation
+
+Please find installation instructions for Caffe2 and Detectron in [`INSTALL.md`](INSTALL.md).
+
+## Quick Start: Using Detectron
+
+After installation, please see [`GETTING_STARTED.md`](GETTING_STARTED.md) for brief tutorials covering inference and training with Detectron.
+
+## Getting Help
+
+To start, please check the [troubleshooting](INSTALL.md#troubleshooting) section of our installation instructions as well as our [FAQ](FAQ.md). If you couldn't find help there, try searching our GitHub issues. We intend the issues page to be a forum in which the community collectively troubleshoots problems.
+
+If bugs are found, **we appreciate pull requests** (including adding Q&A's to `FAQ.md` and improving our installation instructions and troubleshooting documents). Please see [CONTRIBUTING.md](CONTRIBUTING.md) for more information about contributing to Detectron.
+
+## References
+
+- [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/abs/1712.04440).
+  Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He.
+  Tech report, arXiv, Dec. 2017.
+- [Learning to Segment Every Thing](https://arxiv.org/abs/1711.10370).
+  Ronghang Hu, Piotr Dollár, Kaiming He, Trevor Darrell, and Ross Girshick.
+  Tech report, arXiv, Nov. 2017.
+- [Non-Local Neural Networks](https://arxiv.org/abs/1711.07971).
+  Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He.
+  Tech report, arXiv, Nov. 2017.
+- [Mask R-CNN](https://arxiv.org/abs/1703.06870).
+  Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick.
+  IEEE International Conference on Computer Vision (ICCV), 2017.
+- [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002).
+  Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár.
+  IEEE International Conference on Computer Vision (ICCV), 2017.
+- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677).
+  Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He.
+  Tech report, arXiv, June 2017.
+- [Detecting and Recognizing Human-Object Interactions](https://arxiv.org/abs/1704.07333).
+  Georgia Gkioxari, Ross Girshick, Piotr Dollár, and Kaiming He.
+  Tech report, arXiv, Apr. 2017.
+- [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144).
+  Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie.
+  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
+- [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431).
+  Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He.
+  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
+- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](http://arxiv.org/abs/1605.06409).
+  Jifeng Dai, Yi Li, Kaiming He, and Jian Sun.
+  Conference on Neural Information Processing Systems (NIPS), 2016.
+- [Deep Residual Learning for Image Recognition](http://arxiv.org/abs/1512.03385).
+  Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.
+  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
+- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](http://arxiv.org/abs/1506.01497)
+  Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun.
+  Conference on Neural Information Processing Systems (NIPS), 2015.
+- [Fast R-CNN](http://arxiv.org/abs/1504.08083).
+  Ross Girshick.
+  IEEE International Conference on Computer Vision (ICCV), 2015.

configs/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_faster_rcnn_R-50-C4_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: ResNet.add_ResNet50_conv4_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+RPN:
+  SIZES: (32, 64, 128, 256, 512)
+FAST_RCNN:
+  ROI_BOX_HEAD: ResNet.add_ResNet_roi_conv5_head
+  ROI_XFORM_METHOD: RoIAlign
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 6000
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_faster_rcnn_R-50-C4_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: ResNet.add_ResNet50_conv4_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+RPN:
+  SIZES: (32, 64, 128, 256, 512)
+FAST_RCNN:
+  ROI_BOX_HEAD: ResNet.add_ResNet_roi_conv5_head
+  ROI_XFORM_METHOD: RoIAlign
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 6000
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_faster_rcnn_X-101-64x4d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_faster_rcnn_X-101-64x4d-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  # md5sum of weights pkl file: aa14062280226e48f569ef1c7212e7c7
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_keypoint_rcnn_R-101-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  FASTER_RCNN: True
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_keypoint_rcnn_R-101-FPN_s1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  FASTER_RCNN: True
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 130000
+  STEPS: [0, 100000, 120000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 2
+  FASTER_RCNN: True
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_s1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 2
+  FASTER_RCNN: True
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 130000
+  STEPS: [0, 100000, 120000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_keypoint_rcnn_X-101-32x8d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  FASTER_RCNN: True
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_keypoint_rcnn_X-101-32x8d-FPN_s1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  FASTER_RCNN: True
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 130000
+  STEPS: [0, 100000, 120000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_keypoint_rcnn_X-101-64x4d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  FASTER_RCNN: True
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  # md5sum of weights pkl file: aa14062280226e48f569ef1c7212e7c7
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_keypoint_rcnn_X-101-64x4d-FPN_s1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  FASTER_RCNN: True
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 130000
+  STEPS: [0, 100000, 120000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  # md5sum of weights pkl file: aa14062280226e48f569ef1c7212e7c7
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_R-50-C4_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: ResNet.add_ResNet50_conv4_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+RPN:
+  SIZES: (32, 64, 128, 256, 512)
+FAST_RCNN:
+  ROI_BOX_HEAD: ResNet.add_ResNet_roi_conv5_head
+  ROI_XFORM_METHOD: RoIAlign
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v0upshare
+  RESOLUTION: 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default: GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 6000
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_R-50-C4_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: ResNet.add_ResNet50_conv4_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+RPN:
+  SIZES: (32, 64, 128, 256, 512)
+FAST_RCNN:
+  ROI_BOX_HEAD: ResNet.add_ResNet_roi_conv5_head
+  ROI_XFORM_METHOD: RoIAlign
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v0upshare
+  RESOLUTION: 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default: GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 6000
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_X-101-64x4d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  # md5sum of weights pkl file: aa14062280226e48f569ef1c7212e7c7
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_X-101-64x4d-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  # md5sum of weights pkl file: aa14062280226e48f569ef1c7212e7c7
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/12_2017_baselines/e2e_mask_rcnn_X-152-32x8d-FPN-IN5k_1.44x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet152_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 1.44x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 260000
+  STEPS: [0, 200000, 240000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (640, 672, 704, 736, 768, 800)  # Scale jitter
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  BBOX_VOTE:
+    ENABLED: True
+    VOTE_TH: 0.9
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+  BBOX_AUG:
+    ENABLED: True
+    SCORE_HEUR: UNION
+    COORD_HEUR: UNION
+    H_FLIP: True
+    SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200)
+    MAX_SIZE: 2000
+    SCALE_H_FLIP: True
+    SCALE_SIZE_DEP: False
+    ASPECT_RATIOS: ()
+    ASPECT_RATIO_H_FLIP: False
+  MASK_AUG:
+    ENABLED: True
+    HEUR: SOFT_AVG
+    H_FLIP: True
+    SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200)
+    MAX_SIZE: 2000
+    SCALE_H_FLIP: True
+    SCALE_SIZE_DEP: False
+    ASPECT_RATIOS: ()
+    ASPECT_RATIO_H_FLIP: False
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_R-101-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_R-101-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_R-50-C4_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: ResNet.add_ResNet50_conv4_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+RPN:
+  SIZES: (32, 64, 128, 256, 512)
+FAST_RCNN:
+  ROI_BOX_HEAD: ResNet.add_ResNet_roi_conv5_head
+  ROI_XFORM_METHOD: RoIAlign
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_train/rpn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_valminusminival/rpn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_minival/rpn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_R-50-C4_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: ResNet.add_ResNet50_conv4_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+RPN:
+  SIZES: (32, 64, 128, 256, 512)
+FAST_RCNN:
+  ROI_BOX_HEAD: ResNet.add_ResNet_roi_conv5_head
+  ROI_XFORM_METHOD: RoIAlign
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_train/rpn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_valminusminival/rpn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_minival/rpn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_R-50-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_X-101-32x8d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_X-101-32x8d-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_X-101-64x4d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/fast_rcnn_X-101-64x4d-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/keypoint_rcnn_R-101-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35999521/12_2017_baselines/rpn_person_only_R-101-FPN_1x.yaml.08_20_33.1OkqMmqP/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35999521/12_2017_baselines/rpn_person_only_R-101-FPN_1x.yaml.08_20_33.1OkqMmqP/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35999521/12_2017_baselines/rpn_person_only_R-101-FPN_1x.yaml.08_20_33.1OkqMmqP/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/keypoint_rcnn_R-101-FPN_s1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 130000
+  STEPS: [0, 100000, 120000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35999521/12_2017_baselines/rpn_person_only_R-101-FPN_1x.yaml.08_20_33.1OkqMmqP/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35999521/12_2017_baselines/rpn_person_only_R-101-FPN_1x.yaml.08_20_33.1OkqMmqP/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35999521/12_2017_baselines/rpn_person_only_R-101-FPN_1x.yaml.08_20_33.1OkqMmqP/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/keypoint_rcnn_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 2
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/keypoint_rcnn_R-50-FPN_s1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 2
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 130000
+  STEPS: [0, 100000, 120000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/keypoint_rcnn_X-101-32x8d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760438/12_2017_baselines/rpn_person_only_X-101-32x8d-FPN_1x.yaml.06_04_23.M2oJlDPW/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/36760438/12_2017_baselines/rpn_person_only_X-101-32x8d-FPN_1x.yaml.06_04_23.M2oJlDPW/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760438/12_2017_baselines/rpn_person_only_X-101-32x8d-FPN_1x.yaml.06_04_23.M2oJlDPW/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/keypoint_rcnn_X-101-32x8d-FPN_s1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 130000
+  STEPS: [0, 100000, 120000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760438/12_2017_baselines/rpn_person_only_X-101-32x8d-FPN_1x.yaml.06_04_23.M2oJlDPW/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/36760438/12_2017_baselines/rpn_person_only_X-101-32x8d-FPN_1x.yaml.06_04_23.M2oJlDPW/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760438/12_2017_baselines/rpn_person_only_X-101-32x8d-FPN_1x.yaml.06_04_23.M2oJlDPW/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/keypoint_rcnn_X-101-64x4d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  # md5sum of weights pkl file: aa14062280226e48f569ef1c7212e7c7
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35999553/12_2017_baselines/rpn_person_only_X-101-64x4d-FPN_1x.yaml.08_21_33.ghFzzArr/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35999553/12_2017_baselines/rpn_person_only_X-101-64x4d-FPN_1x.yaml.08_21_33.ghFzzArr/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35999553/12_2017_baselines/rpn_person_only_X-101-64x4d-FPN_1x.yaml.08_21_33.ghFzzArr/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/keypoint_rcnn_X-101-64x4d-FPN_s1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 130000
+  STEPS: [0, 100000, 120000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: head_builder.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  # md5sum of weights pkl file: aa14062280226e48f569ef1c7212e7c7
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35999553/12_2017_baselines/rpn_person_only_X-101-64x4d-FPN_1x.yaml.08_21_33.ghFzzArr/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35999553/12_2017_baselines/rpn_person_only_X-101-64x4d-FPN_1x.yaml.08_21_33.ghFzzArr/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35999553/12_2017_baselines/rpn_person_only_X-101-64x4d-FPN_1x.yaml.08_21_33.ghFzzArr/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_R-101-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_R-101-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998887/12_2017_baselines/rpn_R-101-FPN_1x.yaml.08_07_07.vzhHEs0V/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_R-50-C4_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: ResNet.add_ResNet50_conv4_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+RPN:
+  SIZES: (32, 64, 128, 256, 512)
+FAST_RCNN:
+  ROI_BOX_HEAD: ResNet.add_ResNet_roi_conv5_head
+  ROI_XFORM_METHOD: RoIAlign
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v0upshare
+  RESOLUTION: 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default: GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_train/rpn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_valminusminival/rpn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_minival/rpn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_R-50-C4_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: ResNet.add_ResNet50_conv4_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+RPN:
+  SIZES: (32, 64, 128, 256, 512)
+FAST_RCNN:
+  ROI_BOX_HEAD: ResNet.add_ResNet_roi_conv5_head
+  ROI_XFORM_METHOD: RoIAlign
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v0upshare
+  RESOLUTION: 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default: GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_train/rpn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_valminusminival/rpn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L/output/test/coco_2014_minival/rpn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_R-50-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_X-101-32x8d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_X-101-32x8d-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/36760102/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml.06_00_16.RWeBAniO/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_X-101-64x4d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  # md5sum of weights pkl file: aa14062280226e48f569ef1c7212e7c7
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/mask_rcnn_X-101-64x4d-FPN_2x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  # 2x schedule (note TRAIN.IMS_PER_BATCH: 1)
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 360000
+  STEPS: [0, 240000, 320000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  # md5sum of weights pkl file: aa14062280226e48f569ef1c7212e7c7
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  IMS_PER_BATCH: 1
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998956/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml.08_08_41.Seh0psKz/output/test/coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+OUTPUT_DIR: .

configs/12_2017_baselines/retinanet_R-101-FPN_1x.yaml

+MODEL:
+  TYPE: retinanet
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 7
+  RPN_MIN_LEVEL: 3
+  COARSEST_STRIDE: 128
+  EXTRA_CONV_LEVELS: True
+RETINANET:
+  RETINANET_ON: True
+  NUM_CONVS: 4
+  ASPECT_RATIOS: (1.0, 2.0, 0.5)
+  SCALES_PER_OCTAVE: 3
+  ANCHOR_SCALE: 4
+  LOSS_GAMMA: 2.0
+  LOSS_ALPHA: 0.25
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_STRADDLE_THRESH: -1  # default 0
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 10000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/retinanet_R-101-FPN_2x.yaml

+MODEL:
+  TYPE: retinanet
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 7
+  RPN_MIN_LEVEL: 3
+  COARSEST_STRIDE: 128
+  EXTRA_CONV_LEVELS: True
+RETINANET:
+  RETINANET_ON: True
+  NUM_CONVS: 4
+  ASPECT_RATIOS: (1.0, 2.0, 0.5)
+  SCALES_PER_OCTAVE: 3
+  ANCHOR_SCALE: 4
+  LOSS_GAMMA: 2.0
+  LOSS_ALPHA: 0.25
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_STRADDLE_THRESH: -1  # default 0
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 10000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/retinanet_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: retinanet
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 7
+  RPN_MIN_LEVEL: 3
+  COARSEST_STRIDE: 128
+  EXTRA_CONV_LEVELS: True
+RETINANET:
+  RETINANET_ON: True
+  NUM_CONVS: 4
+  ASPECT_RATIOS: (1.0, 2.0, 0.5)
+  SCALES_PER_OCTAVE: 3
+  ANCHOR_SCALE: 4
+  LOSS_GAMMA: 2.0
+  LOSS_ALPHA: 0.25
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_STRADDLE_THRESH: -1  # default 0
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 10000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/retinanet_R-50-FPN_2x.yaml

+MODEL:
+  TYPE: retinanet
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 7
+  RPN_MIN_LEVEL: 3
+  COARSEST_STRIDE: 128
+  EXTRA_CONV_LEVELS: True
+RETINANET:
+  RETINANET_ON: True
+  NUM_CONVS: 4
+  ASPECT_RATIOS: (1.0, 2.0, 0.5)
+  SCALES_PER_OCTAVE: 3
+  ANCHOR_SCALE: 4
+  LOSS_GAMMA: 2.0
+  LOSS_ALPHA: 0.25
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_STRADDLE_THRESH: -1  # default 0
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 10000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/retinanet_X-101-32x8d-FPN_1x.yaml

+MODEL:
+  TYPE: retinanet
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 7
+  RPN_MIN_LEVEL: 3
+  COARSEST_STRIDE: 128
+  EXTRA_CONV_LEVELS: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+RETINANET:
+  RETINANET_ON: True
+  NUM_CONVS: 4
+  ASPECT_RATIOS: (1.0, 2.0, 0.5)
+  SCALES_PER_OCTAVE: 3
+  ANCHOR_SCALE: 4
+  LOSS_GAMMA: 2.0
+  LOSS_ALPHA: 0.25
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_STRADDLE_THRESH: -1  # default 0
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 10000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/retinanet_X-101-32x8d-FPN_2x.yaml

+MODEL:
+  TYPE: retinanet
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 7
+  RPN_MIN_LEVEL: 3
+  COARSEST_STRIDE: 128
+  EXTRA_CONV_LEVELS: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+RETINANET:
+  RETINANET_ON: True
+  NUM_CONVS: 4
+  ASPECT_RATIOS: (1.0, 2.0, 0.5)
+  SCALES_PER_OCTAVE: 3
+  ANCHOR_SCALE: 4
+  LOSS_GAMMA: 2.0
+  LOSS_ALPHA: 0.25
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_STRADDLE_THRESH: -1  # default 0
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 10000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/retinanet_X-101-64x4d-FPN_1x.yaml

+MODEL:
+  TYPE: retinanet
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 7
+  RPN_MIN_LEVEL: 3
+  COARSEST_STRIDE: 128
+  EXTRA_CONV_LEVELS: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+RETINANET:
+  RETINANET_ON: True
+  NUM_CONVS: 4
+  ASPECT_RATIOS: (1.0, 2.0, 0.5)
+  SCALES_PER_OCTAVE: 3
+  ANCHOR_SCALE: 4
+  LOSS_GAMMA: 2.0
+  LOSS_ALPHA: 0.25
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_STRADDLE_THRESH: -1  # default 0
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 10000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/retinanet_X-101-64x4d-FPN_2x.yaml

+MODEL:
+  TYPE: retinanet
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 7
+  RPN_MIN_LEVEL: 3
+  COARSEST_STRIDE: 128
+  EXTRA_CONV_LEVELS: True
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+RETINANET:
+  RETINANET_ON: True
+  NUM_CONVS: 4
+  ASPECT_RATIOS: (1.0, 2.0, 0.5)
+  SCALES_PER_OCTAVE: 3
+  ANCHOR_SCALE: 4
+  LOSS_GAMMA: 2.0
+  LOSS_ALPHA: 0.25
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_STRADDLE_THRESH: -1  # default 0
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 10000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/rpn_R-101-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  RPN_ONLY: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 6
+  RPN_MIN_LEVEL: 2
+  RPN_ANCHOR_START_SIZE: 32
+  RPN_ASPECT_RATIOS: (0.5, 1, 2)
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+TEST:
+  DATASETS: ('coco_2014_minival','coco_2014_train','coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/rpn_R-50-C4_1x.yaml

+MODEL:
+  TYPE: rpn
+  CONV_BODY: ResNet.add_ResNet50_conv4_body
+  NUM_CLASSES: 81
+  RPN_ONLY: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+RPN:
+  SIZES: (32, 64, 128, 256, 512)
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+TEST:
+  DATASETS: ('coco_2014_minival','coco_2014_train','coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+USE_NCCL: False
+OUTPUT_DIR: .

configs/12_2017_baselines/rpn_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  RPN_ONLY: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 6
+  RPN_MIN_LEVEL: 2
+  RPN_ANCHOR_START_SIZE: 32
+  RPN_ASPECT_RATIOS: (0.5, 1, 2)
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+TEST:
+  DATASETS: ('coco_2014_minival','coco_2014_train','coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/rpn_X-101-32x8d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  RPN_ONLY: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 6
+  RPN_MIN_LEVEL: 2
+  RPN_ANCHOR_START_SIZE: 32
+  RPN_ASPECT_RATIOS: (0.5, 1, 2)
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+TEST:
+  DATASETS: ('coco_2014_minival','coco_2014_train','coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/rpn_X-101-64x4d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 81
+  RPN_ONLY: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 6
+  RPN_MIN_LEVEL: 2
+  RPN_ANCHOR_START_SIZE: 32
+  RPN_ASPECT_RATIOS: (0.5, 1, 2)
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+TEST:
+  DATASETS: ('coco_2014_minival','coco_2014_train','coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/rpn_person_only_R-101-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  RPN_ONLY: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 6
+  RPN_MIN_LEVEL: 2
+  RPN_ANCHOR_START_SIZE: 32
+  RPN_ASPECT_RATIOS: (0.5, 1, 2)
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival', 'keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival', 'keypoints_coco_2015_test')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 2
+  RPN_ONLY: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 6
+  RPN_MIN_LEVEL: 2
+  RPN_ANCHOR_START_SIZE: 32
+  RPN_ASPECT_RATIOS: (0.5, 1, 2)
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival', 'keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival', 'keypoints_coco_2015_test')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/rpn_person_only_X-101-32x8d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  RPN_ONLY: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 6
+  RPN_MIN_LEVEL: 2
+  RPN_ANCHOR_START_SIZE: 32
+  RPN_ASPECT_RATIOS: (0.5, 1, 2)
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 32
+  WIDTH_PER_GROUP: 8
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival', 'keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival', 'keypoints_coco_2015_test')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/12_2017_baselines/rpn_person_only_X-101-64x4d-FPN_1x.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
+  NUM_CLASSES: 2
+  RPN_ONLY: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_RPN: True
+  RPN_MAX_LEVEL: 6
+  RPN_MIN_LEVEL: 2
+  RPN_ANCHOR_START_SIZE: 32
+  RPN_ASPECT_RATIOS: (0.5, 1, 2)
+RESNETS:
+  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
+  TRANS_FUNC: bottleneck_transformation
+  NUM_GROUPS: 64
+  WIDTH_PER_GROUP: 4
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival', 'keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival', 'keypoints_coco_2015_test')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 2000
+OUTPUT_DIR: .

configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 1
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.0025
+  GAMMA: 0.1
+  MAX_ITER: 60000
+  STEPS: [0, 30000, 40000]
+  # Equivalent schedules with...
+  # 1 GPU:
+  #   BASE_LR: 0.0025
+  #   MAX_ITER: 60000
+  #   STEPS: [0, 30000, 40000]
+  # 2 GPUs:
+  #   BASE_LR: 0.005
+  #   MAX_ITER: 30000
+  #   STEPS: [0, 15000, 20000]
+  # 4 GPUs:
+  #   BASE_LR: 0.01
+  #   MAX_ITER: 15000
+  #   STEPS: [0, 7500, 10000]
+  # 8 GPUs:
+  #   BASE_LR: 0.02
+  #   MAX_ITER: 7500
+  #   STEPS: [0, 3750, 5000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train',)
+  SCALES: (500,)
+  MAX_SIZE: 833
+  BATCH_SIZE_PER_IM: 256
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (500,)
+  MAX_SIZE: 833
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/getting_started/tutorial_2gpu_e2e_faster_rcnn_R-50-FPN.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 2
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.005
+  GAMMA: 0.1
+  MAX_ITER: 30000
+  STEPS: [0, 15000, 20000]
+  # Equivalent schedules with...
+  # 1 GPU:
+  #   BASE_LR: 0.0025
+  #   MAX_ITER: 60000
+  #   STEPS: [0, 30000, 40000]
+  # 2 GPUs:
+  #   BASE_LR: 0.005
+  #   MAX_ITER: 30000
+  #   STEPS: [0, 15000, 20000]
+  # 4 GPUs:
+  #   BASE_LR: 0.01
+  #   MAX_ITER: 15000
+  #   STEPS: [0, 7500, 10000]
+  # 8 GPUs:
+  #   BASE_LR: 0.02
+  #   MAX_ITER: 7500
+  #   STEPS: [0, 3750, 5000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train',)
+  SCALES: (500,)
+  MAX_SIZE: 833
+  BATCH_SIZE_PER_IM: 256
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (500,)
+  MAX_SIZE: 833
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/getting_started/tutorial_4gpu_e2e_faster_rcnn_R-50-FPN.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 4
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.01
+  GAMMA: 0.1
+  MAX_ITER: 15000
+  STEPS: [0, 7500, 10000]
+  # Equivalent schedules with...
+  # 1 GPU:
+  #   BASE_LR: 0.0025
+  #   MAX_ITER: 60000
+  #   STEPS: [0, 30000, 40000]
+  # 2 GPUs:
+  #   BASE_LR: 0.005
+  #   MAX_ITER: 30000
+  #   STEPS: [0, 15000, 20000]
+  # 4 GPUs:
+  #   BASE_LR: 0.01
+  #   MAX_ITER: 15000
+  #   STEPS: [0, 7500, 10000]
+  # 8 GPUs:
+  #   BASE_LR: 0.02
+  #   MAX_ITER: 7500
+  #   STEPS: [0, 3750, 5000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train',)
+  SCALES: (500,)
+  MAX_SIZE: 833
+  BATCH_SIZE_PER_IM: 256
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (500,)
+  MAX_SIZE: 833
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/getting_started/tutorial_8gpu_e2e_faster_rcnn_R-50-FPN.yaml

+MODEL:
+  TYPE: generalized_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 7500
+  STEPS: [0, 3750, 5000]
+  # Equivalent schedules with...
+  # 1 GPU:
+  #   BASE_LR: 0.0025
+  #   MAX_ITER: 60000
+  #   STEPS: [0, 30000, 40000]
+  # 2 GPUs:
+  #   BASE_LR: 0.005
+  #   MAX_ITER: 30000
+  #   STEPS: [0, 15000, 20000]
+  # 4 GPUs:
+  #   BASE_LR: 0.01
+  #   MAX_ITER: 15000
+  #   STEPS: [0, 7500, 10000]
+  # 8 GPUs:
+  #   BASE_LR: 0.02
+  #   MAX_ITER: 7500
+  #   STEPS: [0, 3750, 5000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train',)
+  SCALES: (500,)
+  MAX_SIZE: 833
+  BATCH_SIZE_PER_IM: 256
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (500,)
+  MAX_SIZE: 833
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+OUTPUT_DIR: .

configs/test_time_aug/e2e_mask_rcnn_R-50-FPN_2x.yaml

+MODEL:
+  TYPE: mask_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 81
+  FASTER_RCNN: True
+  MASK_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 180000
+  STEPS: [0, 120000, 160000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+MRCNN:
+  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
+  RESOLUTION: 28  # (output mask resolution) default 14
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14  # default 7
+  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
+  DILATION: 1  # default 2
+  CONV_INIT: MSRAFill  # default GaussianFill
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
+TEST:
+  DATASETS: ('coco_2014_minival',)
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
+  RPN_POST_NMS_TOP_N: 1000
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/35857389/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_2x.yaml.01_37_22.KSeq0b5q/output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl
+
+  # -- Test time augmentation example -- #
+  BBOX_AUG:
+    ENABLED: True
+    SCORE_HEUR: UNION  # AVG NOTE: cannot use AVG for e2e model
+    COORD_HEUR: UNION  # AVG NOTE: cannot use AVG for e2e model
+    H_FLIP: True
+    SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200)
+    MAX_SIZE: 2000
+    SCALE_H_FLIP: True
+    SCALE_SIZE_DEP: False
+    AREA_TH_LO: 2500   # 50^2
+    AREA_TH_HI: 32400  # 180^2
+    ASPECT_RATIOS: ()
+    ASPECT_RATIO_H_FLIP: False
+  MASK_AUG:
+    ENABLED: True
+    HEUR: SOFT_AVG
+    H_FLIP: True
+    SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200)
+    MAX_SIZE: 2000
+    SCALE_H_FLIP: True
+    SCALE_SIZE_DEP: False
+    AREA_TH: 32400  # 180^2
+    ASPECT_RATIOS: ()
+    ASPECT_RATIO_H_FLIP: False
+  BBOX_VOTE:
+    ENABLED: True
+    VOTE_TH: 0.9
+  # -- Test time augmentation example -- #
+
+USE_NCCL: False
+OUTPUT_DIR: .

configs/test_time_aug/keypoint_rcnn_R-50-FPN_1x.yaml

+MODEL:
+  TYPE: keypoint_rcnn
+  CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
+  NUM_CLASSES: 2
+  KEYPOINTS_ON: True
+NUM_GPUS: 8
+SOLVER:
+  WEIGHT_DECAY: 0.0001
+  LR_POLICY: steps_with_decay
+  BASE_LR: 0.02
+  GAMMA: 0.1
+  MAX_ITER: 90000
+  STEPS: [0, 60000, 80000]
+FPN:
+  FPN_ON: True
+  MULTILEVEL_ROIS: True
+  MULTILEVEL_RPN: True  # accidentally True; disable in the future
+FAST_RCNN:
+  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 7
+  ROI_XFORM_SAMPLING_RATIO: 2
+KRCNN:
+  ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
+  NUM_STACKED_CONVS: 8
+  NUM_KEYPOINTS: 17
+  USE_DECONV_OUTPUT: True
+  CONV_INIT: MSRAFill
+  CONV_HEAD_DIM: 512
+  UP_SCALE: 2
+  HEATMAP_SIZE: 56  # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
+  ROI_XFORM_METHOD: RoIAlign
+  ROI_XFORM_RESOLUTION: 14
+  ROI_XFORM_SAMPLING_RATIO: 2
+  KEYPOINT_CONFIDENCE: bbox
+TRAIN:
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl', 'https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl')
+  SCALES: (640, 672, 704, 736, 768, 800)
+  MAX_SIZE: 1333
+  BATCH_SIZE_PER_IM: 512
+TEST:
+  DATASETS: ('keypoints_coco_2014_minival',)
+  PROPOSAL_FILES: ('https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl',)
+  PROPOSAL_LIMIT: 1000
+  SCALES: (800,)
+  MAX_SIZE: 1333
+  NMS: 0.5
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/37651887/12_2017_baselines/keypoint_rcnn_R-50-FPN_s1x.yaml.20_01_40.FDjUQ7VX/output/train/keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival/generalized_rcnn/model_final.pkl
+
+  # -- Test time augmentation example -- #
+  BBOX_AUG:
+    ENABLED: True
+    SCORE_HEUR: AVG
+    COORD_HEUR: AVG
+    H_FLIP: True
+    SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200)
+    MAX_SIZE: 2000
+    SCALE_H_FLIP: True
+    SCALE_SIZE_DEP: False
+    AREA_TH_LO: 2500  # 50^2
+    AREA_TH_HI: 32400  # 180^2
+  KPS_AUG:
+    ENABLED: True
+    HEUR: HM_AVG
+    H_FLIP: True
+    SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200)
+    MAX_SIZE: 2000
+    SCALE_H_FLIP: True
+    SCALE_SIZE_DEP: True
+    AREA_TH: 22500  # 150^2
+    ASPECT_RATIOS: ()
+    ASPECT_RATIO_H_FLIP: False
+  # -- Test time augmentation example -- #
+
+OUTPUT_DIR: .

demo/NOTICE

+The demo images are licensed as United States government work:
+https://www.usa.gov/government-works
+
+The image files were obtained on Jan 13, 2018 from the following
+URLs.
+
+16004479832_a748d55f21_k.jpg
+https://www.flickr.com/photos/archivesnews/16004479832
+
+18124840932_e42b3e377c_k.jpg
+https://www.flickr.com/photos/usnavy/18124840932
+
+33887522274_eebd074106_k.jpg
+https://www.flickr.com/photos/usaid_pakistan/33887522274
+
+15673749081_767a7fa63a_k.jpg
+https://www.flickr.com/photos/usnavy/15673749081
+
+34501842524_3c858b3080_k.jpg
+https://www.flickr.com/photos/departmentofenergy/34501842524
+
+24274813513_0cfd2ce6d0_k.jpg
+https://www.flickr.com/photos/dhsgov/24274813513
+
+19064748793_bb942deea1_k.jpg
+https://www.flickr.com/photos/statephotos/19064748793
+
+33823288584_1d21cf0a26_k.jpg
+https://www.flickr.com/photos/cbpphotos/33823288584
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