Add COCO_2017 in paths_catalog.py (#373)

* Add new section "Projects using maskrcnn-benchmark".

* Update README.md

update the format.

* Update README.md

* Add coco_2017_train and coco_2017_val

* Update README.md

Add the instructions about COCO_2017

README.md

@@ -83,10 +83,19 @@ ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
 ln -s /path_to_coco_dataset/train2014 datasets/coco/train2014
 ln -s /path_to_coco_dataset/test2014 datasets/coco/test2014
 ln -s /path_to_coco_dataset/val2014 datasets/coco/val2014
+# or use COCO 2017 version
+ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
+ln -s /path_to_coco_dataset/train2017 datasets/coco/train2017
+ln -s /path_to_coco_dataset/test2017 datasets/coco/test2017
+ln -s /path_to_coco_dataset/val2017 datasets/coco/val2017
+
 # for pascal voc dataset:
 ln -s /path_to_VOCdevkit_dir datasets/voc
 ```
 
+P.S. `COCO_2017_train` = `COCO_2014_train` + `valminusminival` , `COCO_2017_val` = `minival`
+      
+
 You can also configure your own paths to the datasets.
 For that, all you need to do is to modify `maskrcnn_benchmark/config/paths_catalog.py` to
 point to the location where your dataset is stored.

maskrcnn_benchmark/config/paths_catalog.py

@@ -7,6 +7,14 @@ import os
 class DatasetCatalog(object):
     DATA_DIR = "datasets"
     DATASETS = {
+        "coco_2017_train": {
+            "img_dir": "coco/train2017",
+            "ann_file": "coco/annotations/instances_train2017.json"
+        },
+        "coco_2017_val": {
+            "img_dir": "coco/val2017",
+            "ann_file": "coco/annotations/instances_val2017.json"
+        },
         "coco_2014_train": {
             "img_dir": "coco/train2014",
             "ann_file": "coco/annotations/instances_train2014.json"