Filter Coco Dataset

This notebook shows how to create a custom dataset starting from COCO>

/Users/fumarolaf/miniconda3/envs/an/lib/python3.7/site-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using tqdm.autonotebook.tqdm in notebook mode. Use tqdm.tqdm instead to force console mode (e.g. in jupyter console)
  " (e.g. in jupyter console)", TqdmExperimentalWarning)

Load the coco dataset. > download the annotations from [coco datasets](http://cocodataset.org/#download

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

Show the number of images for category

[('person', 64115), ('chair', 12774), ('car', 12251), ('dining table', 11837), ('cup', 9189), ('bottle', 8501), ('bowl', 7111), ('handbag', 6841), ('truck', 6127), ('bench', 5570), ('backpack', 5528), ('book', 5332), ('cell phone', 4803), ('sink', 4678), ('clock', 4659), ('tv', 4561), ('potted plant', 4452), ('couch', 4423), ('dog', 4385), ('knife', 4326), ('sports ball', 4262), ('traffic light', 4139), ('cat', 4114), ('umbrella', 3968), ('bus', 3952), ('tie', 3810), ('bed', 3682), ('vase', 3593), ('train', 3588), ('fork', 3555), ('spoon', 3529), ('laptop', 3524), ('motorcycle', 3502), ('surfboard', 3486), ('skateboard', 3476), ('tennis racket', 3394), ('toilet', 3353), ('bicycle', 3252), ('bird', 3237), ('pizza', 3166), ('skis', 3082), ('remote', 3076), ('boat', 3025), ('airplane', 2986), ('horse', 2941), ('cake', 2925), ('oven', 2877), ('baseball glove', 2629), ('giraffe', 2546), ('wine glass', 2533), ('baseball bat', 2506), ('suitcase', 2402), ('sandwich', 2365), ('refrigerator', 2360), ('kite', 2261), ('banana', 2243), ('frisbee', 2184), ('elephant', 2143), ('teddy bear', 2140), ('keyboard', 2115), ('cow', 1968), ('broccoli', 1939), ('zebra', 1916), ('mouse', 1876), ('stop sign', 1734), ('fire hydrant', 1711), ('orange', 1699), ('carrot', 1683), ('snowboard', 1654), ('apple', 1586), ('microwave', 1547), ('sheep', 1529), ('donut', 1523), ('hot dog', 1222), ('toothbrush', 1007), ('bear', 960), ('scissors', 947), ('parking meter', 705), ('toaster', 217), ('hair drier', 189)]

Show the number of annotations per category

[('person', 262465), ('car', 43867), ('chair', 38491), ('book', 24715), ('bottle', 24342), ('cup', 20650), ('dining table', 15714), ('bowl', 14358), ('traffic light', 12884), ('handbag', 12354), ('umbrella', 11431), ('bird', 10806), ('boat', 10759), ('truck', 9973), ('bench', 9838), ('sheep', 9509), ('banana', 9458), ('kite', 9076), ('motorcycle', 8725), ('backpack', 8720), ('potted plant', 8652), ('cow', 8147), ('wine glass', 7913), ('carrot', 7852), ('knife', 7770), ('broccoli', 7308), ('donut', 7179), ('bicycle', 7113), ('skis', 6646), ('vase', 6613), ('horse', 6587), ('tie', 6496), ('cell phone', 6434), ('orange', 6399), ('cake', 6353), ('sports ball', 6347), ('clock', 6334), ('suitcase', 6192), ('spoon', 6165), ('surfboard', 6126), ('bus', 6069), ('apple', 5851), ('pizza', 5821), ('tv', 5805), ('couch', 5779), ('remote', 5703), ('sink', 5610), ('skateboard', 5543), ('elephant', 5513), ('dog', 5508), ('fork', 5479), ('zebra', 5303), ('airplane', 5135), ('giraffe', 5131), ('laptop', 4970), ('tennis racket', 4812), ('teddy bear', 4793), ('cat', 4768), ('train', 4571), ('sandwich', 4373), ('bed', 4192), ('toilet', 4157), ('baseball glove', 3747), ('oven', 3334), ('baseball bat', 3276), ('hot dog', 2918), ('keyboard', 2855), ('snowboard', 2685), ('frisbee', 2682), ('refrigerator', 2637), ('mouse', 2262), ('stop sign', 1983), ('toothbrush', 1954), ('fire hydrant', 1865), ('microwave', 1673), ('scissors', 1481), ('bear', 1294), ('parking meter', 1285), ('toaster', 225), ('hair drier', 198)]

Filter the Dataset

we create a daset with only 3 categories: - bear - toaster - hair drier

{23, 80, 89}

dict_keys(['info', 'licenses', 'categories', 'annotations', 'images'])

images 1366
annotations 1717

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We can download the images from the web

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removed 0 images