I want to know if there is the clothing object class in the MS COCO dataset?

Question

I want to do something about clothing segmentation, so I need to train the network on dataset which has the clothing class. Anyone can answer me? I do not find something about classes in the http://mscoco.org/

Answer 1

You can find the object instance categories of coco in the instance annotation json files (can be found in the download page).
Download and read the file, you'll see the annotated categories:

In [1]: import json    
In [2]: with open('./instances_val2014.json','r') as R:
   ...:     js = json.loads(R.read())
   ...:
In [3]: js.keys()
Out[3]: [u'info', u'images', u'licenses', u'annotations', u'categories']
In [4]: js['categories']
Out[4]:
[{u'id': 1, u'name': u'person', u'supercategory': u'person'},
 {u'id': 2, u'name': u'bicycle', u'supercategory': u'vehicle'},
 {u'id': 3, u'name': u'car', u'supercategory': u'vehicle'},
 {u'id': 4, u'name': u'motorcycle', u'supercategory': u'vehicle'},
 {u'id': 5, u'name': u'airplane', u'supercategory': u'vehicle'},
 {u'id': 6, u'name': u'bus', u'supercategory': u'vehicle'},
 {u'id': 7, u'name': u'train', u'supercategory': u'vehicle'},
 {u'id': 8, u'name': u'truck', u'supercategory': u'vehicle'},
 {u'id': 9, u'name': u'boat', u'supercategory': u'vehicle'},
 {u'id': 10, u'name': u'traffic light', u'supercategory': u'outdoor'},
 {u'id': 11, u'name': u'fire hydrant', u'supercategory': u'outdoor'},
 {u'id': 13, u'name': u'stop sign', u'supercategory': u'outdoor'},
 {u'id': 14, u'name': u'parking meter', u'supercategory': u'outdoor'},
 {u'id': 15, u'name': u'bench', u'supercategory': u'outdoor'},
 {u'id': 16, u'name': u'bird', u'supercategory': u'animal'},
 {u'id': 17, u'name': u'cat', u'supercategory': u'animal'},
 {u'id': 18, u'name': u'dog', u'supercategory': u'animal'},
 {u'id': 19, u'name': u'horse', u'supercategory': u'animal'},
 {u'id': 20, u'name': u'sheep', u'supercategory': u'animal'},
 {u'id': 21, u'name': u'cow', u'supercategory': u'animal'},
 {u'id': 22, u'name': u'elephant', u'supercategory': u'animal'},
 {u'id': 23, u'name': u'bear', u'supercategory': u'animal'},
 {u'id': 24, u'name': u'zebra', u'supercategory': u'animal'},
 {u'id': 25, u'name': u'giraffe', u'supercategory': u'animal'},
 {u'id': 27, u'name': u'backpack', u'supercategory': u'accessory'},
 {u'id': 28, u'name': u'umbrella', u'supercategory': u'accessory'},
 {u'id': 31, u'name': u'handbag', u'supercategory': u'accessory'},
 {u'id': 32, u'name': u'tie', u'supercategory': u'accessory'},
 {u'id': 33, u'name': u'suitcase', u'supercategory': u'accessory'},
 {u'id': 34, u'name': u'frisbee', u'supercategory': u'sports'},
 {u'id': 35, u'name': u'skis', u'supercategory': u'sports'},
 {u'id': 36, u'name': u'snowboard', u'supercategory': u'sports'},
 {u'id': 37, u'name': u'sports ball', u'supercategory': u'sports'},
 {u'id': 38, u'name': u'kite', u'supercategory': u'sports'},
 {u'id': 39, u'name': u'baseball bat', u'supercategory': u'sports'},
 {u'id': 40, u'name': u'baseball glove', u'supercategory': u'sports'},
 {u'id': 41, u'name': u'skateboard', u'supercategory': u'sports'},
 {u'id': 42, u'name': u'surfboard', u'supercategory': u'sports'},
 {u'id': 43, u'name': u'tennis racket', u'supercategory': u'sports'},
 {u'id': 44, u'name': u'bottle', u'supercategory': u'kitchen'},
 {u'id': 46, u'name': u'wine glass', u'supercategory': u'kitchen'},
 {u'id': 47, u'name': u'cup', u'supercategory': u'kitchen'},
 {u'id': 48, u'name': u'fork', u'supercategory': u'kitchen'},
 {u'id': 49, u'name': u'knife', u'supercategory': u'kitchen'},
 {u'id': 50, u'name': u'spoon', u'supercategory': u'kitchen'},
 {u'id': 51, u'name': u'bowl', u'supercategory': u'kitchen'},
 {u'id': 52, u'name': u'banana', u'supercategory': u'food'},
 {u'id': 53, u'name': u'apple', u'supercategory': u'food'},
 {u'id': 54, u'name': u'sandwich', u'supercategory': u'food'},
 {u'id': 55, u'name': u'orange', u'supercategory': u'food'},
 {u'id': 56, u'name': u'broccoli', u'supercategory': u'food'},
 {u'id': 57, u'name': u'carrot', u'supercategory': u'food'},
 {u'id': 58, u'name': u'hot dog', u'supercategory': u'food'},
 {u'id': 59, u'name': u'pizza', u'supercategory': u'food'},
 {u'id': 60, u'name': u'donut', u'supercategory': u'food'},
 {u'id': 61, u'name': u'cake', u'supercategory': u'food'},
 {u'id': 62, u'name': u'chair', u'supercategory': u'furniture'},
 {u'id': 63, u'name': u'couch', u'supercategory': u'furniture'},
 {u'id': 64, u'name': u'potted plant', u'supercategory': u'furniture'},
 {u'id': 65, u'name': u'bed', u'supercategory': u'furniture'},
 {u'id': 67, u'name': u'dining table', u'supercategory': u'furniture'},
 {u'id': 70, u'name': u'toilet', u'supercategory': u'furniture'},
 {u'id': 72, u'name': u'tv', u'supercategory': u'electronic'},
 {u'id': 73, u'name': u'laptop', u'supercategory': u'electronic'},
 {u'id': 74, u'name': u'mouse', u'supercategory': u'electronic'},
 {u'id': 75, u'name': u'remote', u'supercategory': u'electronic'},
 {u'id': 76, u'name': u'keyboard', u'supercategory': u'electronic'},
 {u'id': 77, u'name': u'cell phone', u'supercategory': u'electronic'},
 {u'id': 78, u'name': u'microwave', u'supercategory': u'appliance'},
 {u'id': 79, u'name': u'oven', u'supercategory': u'appliance'},
 {u'id': 80, u'name': u'toaster', u'supercategory': u'appliance'},
 {u'id': 81, u'name': u'sink', u'supercategory': u'appliance'},
 {u'id': 82, u'name': u'refrigerator', u'supercategory': u'appliance'},
 {u'id': 84, u'name': u'book', u'supercategory': u'indoor'},
 {u'id': 85, u'name': u'clock', u'supercategory': u'indoor'},
 {u'id': 86, u'name': u'vase', u'supercategory': u'indoor'},
 {u'id': 87, u'name': u'scissors', u'supercategory': u'indoor'},
 {u'id': 88, u'name': u'teddy bear', u'supercategory': u'indoor'},
 {u'id': 89, u'name': u'hair drier', u'supercategory': u'indoor'},
 {u'id': 90, u'name': u'toothbrush', u'supercategory': u'indoor'}]

As you can see, apart from 'tie' category, not much clothing there...

On the other hand, you might find DeepFashion dataset useful.