{"action":"created","issue":{"url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/652","repository_url":"https://api.github.com/repos/matterport/Mask_RCNN","labels_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/652/labels{/name}","comments_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/652/comments","events_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/652/events","html_url":"https://github.com/matterport/Mask_RCNN/issues/652","id":330174751,"node_id":"MDU6SXNzdWUzMzAxNzQ3NTE=","number":652,"title":"I need  some advices about training model","user":{"login":"coderclear","id":18438341,"node_id":"MDQ6VXNlcjE4NDM4MzQx","avatar_url":"https://avatars3.githubusercontent.com/u/18438341?v=4","gravatar_id":"","url":"https://api.github.com/users/coderclear","html_url":"https://github.com/coderclear","followers_url":"https://api.github.com/users/coderclear/followers","following_url":"https://api.github.com/users/coderclear/following{/other_user}","gists_url":"https://api.github.com/users/coderclear/gists{/gist_id}","starred_url":"https://api.github.com/users/coderclear/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/coderclear/subscriptions","organizations_url":"https://api.github.com/users/coderclear/orgs","repos_url":"https://api.github.com/users/coderclear/repos","events_url":"https://api.github.com/users/coderclear/events{/privacy}","received_events_url":"https://api.github.com/users/coderclear/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2018-06-07T08:47:35Z","updated_at":"2018-12-20T08:45:13Z","closed_at":null,"author_association":"NONE","body":"I have 4200+ images to train model.  And configuration is \r\nConfigurations:\r\nBACKBONE                       resnet101\r\nBACKBONE_STRIDES               [4, 8, 16, 32, 64]\r\nBATCH_SIZE                     2\r\nBBOX_STD_DEV                   [0.1 0.1 0.2 0.2]\r\nDETECTION_MAX_INSTANCES        100\r\nDETECTION_MIN_CONFIDENCE       0\r\nDETECTION_NMS_THRESHOLD        0.3\r\nGPU_COUNT                      1\r\nGRADIENT_CLIP_NORM             5.0\r\nIMAGES_PER_GPU                 2\r\nIMAGE_MAX_DIM                  512\r\nIMAGE_META_SIZE                14\r\nIMAGE_MIN_DIM                  512\r\nIMAGE_MIN_SCALE                2.0\r\nIMAGE_RESIZE_MODE              crop\r\nIMAGE_SHAPE                    [512 512   3]\r\nLEARNING_MOMENTUM              0.9\r\nLEARNING_RATE                  0.001\r\nLOSS_WEIGHTS                   {'mrcnn_mask_loss': 1.0, 'rpn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0}\r\nMASK_POOL_SIZE                 14\r\nMASK_SHAPE                     [28, 28]\r\nMAX_GT_INSTANCES               100\r\nMEAN_PIXEL                     [123.7 116.8 103.9]\r\nMINI_MASK_SHAPE                (56, 56)\r\nNAME                           davis\r\nNUM_CLASSES                    2\r\nPOOL_SIZE                      7\r\nPOST_NMS_ROIS_INFERENCE        2000\r\nPOST_NMS_ROIS_TRAINING         1000\r\nROI_POSITIVE_RATIO             0.33\r\nRPN_ANCHOR_RATIOS              [0.5, 1, 2]\r\nRPN_ANCHOR_SCALES              (8, 16, 32, 64, 128)\r\nRPN_ANCHOR_STRIDE              1\r\nRPN_BBOX_STD_DEV               [0.1 0.1 0.2 0.2]\r\nRPN_NMS_THRESHOLD              0.8\r\nRPN_TRAIN_ANCHORS_PER_IMAGE    64\r\nSTEPS_PER_EPOCH                2104\r\nTRAIN_BN                       False\r\nTRAIN_ROIS_PER_IMAGE           128\r\nUSE_MINI_MASK                  True\r\nUSE_RPN_ROIS                   True\r\nVALIDATION_STEPS               50\r\nWEIGHT_DECAY                   0.001\r\n\r\ntrain coding is:\r\nmodel = modellib.MaskRCNN(mode=\"training\", config=config,\r\n                          model_dir=MODEL_DIR)\r\ninit_with = \"coco\"  # imagenet, coco, or last\r\nmodel.load_weights(COCO_MODEL_PATH, by_name=True,\r\n                       exclude=[\"mrcnn_class_logits\", \"mrcnn_bbox_fc\", \r\n                                \"mrcnn_bbox\", \"mrcnn_mask\"])\r\n\r\nmodel.train(dataset_train, dataset_val, \r\n            learning_rate=config.LEARNING_RATE, \r\n            epochs=15, \r\n            layers='heads')\r\nmodel.train(dataset_train, dataset_val, \r\n            learning_rate=config.LEARNING_RATE / 10,\r\n            epochs=15, \r\n            layers=\"all\")\r\nloss is \r\n\r\n![image](https://user-images.githubusercontent.com/18438341/41088549-5f7e5c36-6a72-11e8-9fe2-5917e3fe1d3b.png)\r\n\r\n\r\n![image](https://user-images.githubusercontent.com/18438341/41088576-777f48f4-6a72-11e8-89cf-9eaec52c2aef.png)\r\n\r\n\r\nThanks for all your response!\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/comments/448918953","html_url":"https://github.com/matterport/Mask_RCNN/issues/652#issuecomment-448918953","issue_url":"https://api.github.com/repos/matterport/Mask_RCNN/issues/652","id":448918953,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0ODkxODk1Mw==","user":{"login":"lijianaiml","id":40814876,"node_id":"MDQ6VXNlcjQwODE0ODc2","avatar_url":"https://avatars1.githubusercontent.com/u/40814876?v=4","gravatar_id":"","url":"https://api.github.com/users/lijianaiml","html_url":"https://github.com/lijianaiml","followers_url":"https://api.github.com/users/lijianaiml/followers","following_url":"https://api.github.com/users/lijianaiml/following{/other_user}","gists_url":"https://api.github.com/users/lijianaiml/gists{/gist_id}","starred_url":"https://api.github.com/users/lijianaiml/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lijianaiml/subscriptions","organizations_url":"https://api.github.com/users/lijianaiml/orgs","repos_url":"https://api.github.com/users/lijianaiml/repos","events_url":"https://api.github.com/users/lijianaiml/events{/privacy}","received_events_url":"https://api.github.com/users/lijianaiml/received_events","type":"User","site_admin":false},"created_at":"2018-12-20T08:45:13Z","updated_at":"2018-12-20T08:45:13Z","author_association":"NONE","body":"i think your training process may be wrong.In your trian code ,if you want use epoch 15 in first training stage and epoch 15 in second training,you should write like this.\r\n\r\nmodel.train(dataset_train, dataset_val,\r\nlearning_rate=config.LEARNING_RATE,\r\nepochs=15,\r\nlayers='heads')\r\nmodel.train(dataset_train, dataset_val,\r\nlearning_rate=config.LEARNING_RATE / 10,\r\nepochs=30,\r\nlayers=\"all\")"}}
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