{
    "info": {
        "author": "Donggun LEE",
        "author_email": "gnyotnu39@gmail.com",
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            "License :: OSI Approved :: MIT License"
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        "description": "# DeepGeo Extension :: Mask R-CNN\n- Easy Deep Learning\n - Copyright (c) 2019 [InfoLab](http://infolab.kunsan.ac.kr) ([Donggun LEE](http://duration.digimoon.net))\n\n- How to install\n  ```bash\n  pip install deepgeo_ext_maskrcnn\n  ```\n  - other version\n    ```basha\n    # 0.0.1\n    pip install deepgeo_ext_maskrcnn==0.0.1\n    ```\n   - requirement\n      - Python 3.6\n      ```bash\n      pip install deepgeo\n      ```\n- How to use\n   ```python\n   import deepgeo\n\n   engine = deepgeo.Engine()\n   engine.add_model('maskrcnn_mscoco','maskrcnn','D:/default_config.json')\n\n   image = deepgeo.Image.Image(\"image.jpg\",\"D:/Project\")\n   image = engine.detect('maskrcnn_mscoco', image)\n   image.draw_annotations(image.get_annotation())\n   image.save(\"D:/\",\"test\",\"PNG\")\n   ```\n\n- default_config.json\n   ```json\n   {\n     \"BACKBONE\": \"resnet101\",\n     \"BACKBONE_STRIDES\": [\n       4,\n       8,\n       16,\n       32,\n       64\n     ],\n     \"BATCH_SIZE\": 1,\n     \"BBOX_STD_DEV\": [0.1, 0.1, 0.2, 0.2],\n     \"CATEGORY\": [\n       \"bg\",\n       \"person\",\n       \"bicycle\",\n       \"car\",\n       \"motorcycle\",\n       \"airplane\",\n       \"bus\",\n       \"train\",\n       \"truck\",\n       \"boat\",\n       \"traffic_light\",\n       \"fire_hydrant\",\n       \"stop_sign\",\n       \"parking_meter\",\n       \"bench\",\n       \"bird\",\n       \"cat\",\n       \"dog\",\n       \"horse\",\n       \"sheep\",\n       \"cow\",\n       \"elephant\",\n       \"bear\",\n       \"zebra\",\n       \"giraffe\",\n       \"backpack\",\n       \"umbrella\",\n       \"handbag\",\n       \"tie\",\n       \"suitcase\",\n       \"frisbee\",\n       \"skis\",\n       \"snowboard\",\n       \"sports_ball\",\n       \"kite\",\n       \"baseball_bat\",\n       \"baseball_glove\",\n       \"skateboard\",\n       \"surfboard\",\n       \"tennis_racket\",\n       \"bottle\",\n       \"wine_glass\",\n       \"cup\",\n       \"fork\",\n       \"knife\",\n       \"spoon\",\n       \"bowl\",\n       \"banana\",\n       \"apple\",\n       \"sandwich\",\n       \"orange\",\n       \"broccoli\",\n       \"carrot\",\n       \"hot_dog\",\n       \"pizza\",\n       \"donut\",\n       \"cake\",\n       \"chair\",\n       \"couch\",\n       \"potted_plant\",\n       \"bed\",\n       \"dining_table\",\n       \"toilet\",\n       \"tv\",\n       \"laptop\",\n       \"mouse\",\n       \"remote\",\n       \"keyboard\",\n       \"cell_phone\",\n       \"microwave\",\n       \"oven\",\n       \"toaster\",\n       \"sink\",\n       \"refrigerator\",\n       \"book\",\n       \"clock\",\n       \"vase\",\n       \"scissors\",\n       \"teddy_bear\",\n       \"hair_drier\",\n       \"toothbrush\"\n     ],\n     \"COMPUTE_BACKBONE_SHAPE\": null,\n     \"DETECTION_MAX_INSTANCES\": 100,\n     \"DETECTION_MIN_CONFIDENCE\": 0.7,\n     \"DETECTION_NMS_THRESHOLD\": 0.3,\n     \"EPOCHS\": 1,\n     \"FPN_CLASSIF_FC_LAYERS_SIZE\": 1024,\n     \"GPU_COUNT\": 1,\n     \"GRADIENT_CLIP_NORM\": 5.0,\n     \"IMAGES_PER_GPU\": 1,\n     \"IMAGE_CHANNEL_COUNT\": 3,\n     \"IMAGE_MAX_DIM\": 1024,\n     \"IMAGE_META_SIZE\": 14,\n     \"IMAGE_MIN_DIM\": 800,\n     \"IMAGE_MIN_SCALE\": 0,\n     \"IMAGE_PATH\": \"image\",\n     \"IMAGE_RESIZE_MODE\": \"square\",\n     \"IMAGE_SHAPE\": null,\n     \"LAYERS\": \"all\",\n     \"LEARNING_MOMENTUM\": 0.9,\n     \"LEARNING_RATE\": 0.001,\n     \"LOSS_WEIGHTS\": {\n       \"mrcnn_bbox_loss\": 1.0,\n       \"mrcnn_class_loss\": 1.0,\n       \"mrcnn_mask_loss\": 1.0,\n       \"rpn_bbox_loss\": 1.0,\n       \"rpn_class_loss\": 1.0\n     },\n     \"MASK_POOL_SIZE\": 14,\n     \"MASK_SHAPE\": [\n       28,\n       28\n     ],\n     \"MAX_GT_INSTANCES\": 100,\n     \"MEAN_PIXEL\": [123.7, 116.8, 103.9],\n     \"MEMO\": \"\",\n     \"MINI_MASK_SHAPE\": [\n       56,\n       56\n     ],\n     \"MODEL_FILE_NAME\": \"mask_rcnn_coco.h5\",\n     \"MODEL_PATH\":\"model\",\n     \"MODEL_URI\":\"\",\n     \"NAME\": \"MASK_RCNN\",\n     \"NUM_CLASSES\": 80,\n     \"POOL_SIZE\": 7,\n     \"POST_NMS_ROIS_INFERENCE\": 1000,\n     \"POST_NMS_ROIS_TRAINING\": 2000,\n     \"PRE_NMS_LIMIT\": 6000,\n     \"RESULT_TEST_NUM\": 100,\n     \"ROI_POSITIVE_RATIO\": 0.33,\n     \"RPN_ANCHOR_RATIOS\": [\n       0.5,\n       1,\n       2\n     ],\n     \"RPN_ANCHOR_SCALES\": [\n       32,\n       64,\n       128,\n       256,\n       512\n     ],\n     \"RPN_ANCHOR_STRIDE\": 1,\n     \"RPN_BBOX_STD_DEV\": [0.1,0.1,0.2,0.2],\n     \"RPN_NMS_THRESHOLD\": 0.7,\n     \"RPN_TRAIN_ANCHORS_PER_IMAGE\": 256,\n     \"STEPS_PER_EPOCH\": 1000,\n     \"TOP_DOWN_PYRAMID_SIZE\": 256,\n     \"TRAIN_BN\": false,\n     \"TRAIN_ROIS_PER_IMAGE\": 200,\n     \"USE_MINI_MASK\": true,\n     \"USE_RPN_ROIS\": true,\n     \"VALIDATION_STEPS\": 50,\n     \"VERSION\": \"\",\n     \"WEIGHT_DECAY\": 0.0001\n   }\n   ```\n\n",
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