{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# [Sartorius - Cell Instance Segmentation](https://www.kaggle.com/c/petfinder-pawpularity-score)\n> Detect single neuronal cells in microscopy images\n\n![](https://storage.googleapis.com/kaggle-competitions/kaggle/30201/logos/header.png?t=2021-09-03-15-27-46)","metadata":{}},{"cell_type":"markdown","source":"# 📒 Notebooks:\n* Train: [Sartorius: MMDetection [Train]](https://www.kaggle.com/awsaf49/sartorius-mmdetection-train)\n* Infer: [Sartorius: MMDetection [Infer]](https://www.kaggle.com/awsaf49/sartorius-mmdetection-infer)\n* Data:  [Sartorius: COCO Data](https://www.kaggle.com/awsaf49/sartorius-coco-data)","metadata":{}},{"cell_type":"markdown","source":"# Please Upvote If you find this notebook Useful :)","metadata":{}},{"cell_type":"markdown","source":"# 🛠 Install Libraries","metadata":{}},{"cell_type":"code","source":"# dependencies\n!pip install -q /kaggle/input/mmdet-lib-ds-v2/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl\n!pip install -q /kaggle/input/mmdet-lib-ds-v2/torchvision-0.8.0-cp37-cp37m-manylinux1_x86_64.whl\n!pip install -q /kaggle/input/mmdet-lib-ds-v2/yapf-0.32.0-py2.py3-none-any.whl\n!pip install -q /kaggle/input/mmdet-lib-ds-v2/pycocotools-2.0.3/pycocotools-2.0.3.tar\n!pip install -q /kaggle/input/mmdet-lib-ds-v2/mmcv_full-1.4.2-cp37-cp37m-manylinux1_x86_64.whl\n!pip install -q /kaggle/input/mmdet-lib-ds-v2/addict-2.4.0-py3-none-any.whl\n\n\n#wandb\n!pip install -qU wandb","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-12-31T07:06:46.910457Z","iopub.execute_input":"2021-12-31T07:06:46.910777Z","iopub.status.idle":"2021-12-31T07:08:49.512744Z","shell.execute_reply.started":"2021-12-31T07:06:46.910693Z","shell.execute_reply":"2021-12-31T07:08:49.511923Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 📚 Import Libraries","metadata":{}},{"cell_type":"code","source":"from itertools import groupby\nfrom pycocotools import mask as mutils\nfrom pycocotools.coco import COCO\nimport numpy as np\nfrom tqdm.notebook import tqdm\nimport pandas as pd\nimport os\nimport cv2\nimport matplotlib.pyplot as plt\nimport wandb\nfrom PIL import Image\nimport gc\n\nfrom glob import glob\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:08:49.516747Z","iopub.execute_input":"2021-12-31T07:08:49.516972Z","iopub.status.idle":"2021-12-31T07:08:50.350522Z","shell.execute_reply.started":"2021-12-31T07:08:49.516943Z","shell.execute_reply":"2021-12-31T07:08:50.349723Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ⭐ WandB\n<img src=\"https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67\" width=600>\n\nWeights & Biases (W&B) is MLOps platform for tracking our experiemnts. We can use it to Build better models faster with experiment tracking, dataset versioning, and model management. Some of the cool features of W&B:\n\n* Track, compare, and visualize ML experiments\n* Get live metrics, terminal logs, and system stats streamed to the centralized dashboard.\n* Explain how your model works, show graphs of how model versions improved, discuss bugs, and demonstrate progress towards milestones.\n","metadata":{}},{"cell_type":"code","source":"import wandb\n\ntry:\n    from kaggle_secrets import UserSecretsClient\n    user_secrets = UserSecretsClient()\n    api_key = user_secrets.get_secret(\"WANDB\")\n    wandb.login(key=api_key)\n    anonymous = None\nexcept:\n    anonymous = \"must\"\n    print('To use your W&B account,\\nGo to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. \\nGet your W&B access token from here: https://wandb.ai/authorize')","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:08:50.352093Z","iopub.execute_input":"2021-12-31T07:08:50.352345Z","iopub.status.idle":"2021-12-31T07:08:52.107419Z","shell.execute_reply.started":"2021-12-31T07:08:50.352308Z","shell.execute_reply":"2021-12-31T07:08:52.106644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 📖 Meta Data\n\n`tain.csv` - IDs and masks for all training objects. None of this metadata is provided for the test set.\n* `id` - unique identifier for object\n* `annotation` - run length encoded pixels for the identified neuronal cell\n* `width` - source image width\n* `height` - source image height\n* `cell_type` - the cell line\n* `plate_time` - time plate was created\n* `sample_date` - date sample was created\n* `sample_id` - sample identifier\n* `elapsed_timedelta` - time since first image taken of sample\n","metadata":{}},{"cell_type":"code","source":"ROOT = '../input/sartorius-cell-instance-segmentation'\nDATA_DIR = '../input/sartorius-coco-dataset'\nconfig = 'configs/sartorius/custom_config.py'\n\n\n# Train Data\ndf = pd.read_csv(f'{ROOT}/train.csv')\ndf['image_path'] = ROOT + '/train/' + df['id'] + '.png'\ntmp_df = df.drop_duplicates(subset=[\"id\", \"image_path\"]).reset_index(drop=True)\ntmp_df[\"annotation\"] = df.groupby(\"id\")[\"annotation\"].agg(list).reset_index(drop=True)\ndf = tmp_df.copy()\ndf['label']   = df.cell_type.map({v:k for k, v in enumerate(df.cell_type.unique())})\ndf['num_ins'] = df.annotation.map(lambda x: len(x))\ndisplay(df.head(2))","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:08:52.111138Z","iopub.execute_input":"2021-12-31T07:08:52.11134Z","iopub.status.idle":"2021-12-31T07:08:52.790521Z","shell.execute_reply.started":"2021-12-31T07:08:52.111316Z","shell.execute_reply":"2021-12-31T07:08:52.789862Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🌈 Visualization\n","metadata":{}},{"cell_type":"code","source":"annFile = f'{DATA_DIR}/annotations_train.json'\ncoco    = COCO(annFile)\nimgIds  = coco.getImgIds()\n\ntmp_df = df.query(\"num_ins<=30 and num_ins>=15\").head(2)\n_,axs = plt.subplots(len(tmp_df),2,figsize=(40,15 * len(tmp_df)))\nfor (_, row), ax in zip(tmp_df.iterrows(), axs):\n    img = cv2.imread(DATA_DIR+f'/train2017/{row.id}.png')\n    img_img = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8)).apply(img[...,0])\n    annIds  = coco.getAnnIds(imgIds=[row.id])\n    anns    = coco.loadAnns(annIds)\n    ax[0].imshow(img)\n    ax[1].imshow(img)\n    plt.sca(ax[1])\n    coco.showAnns(anns, draw_bbox=True)\nplt.tight_layout()\nplt.show()\ndel coco; gc.collect()","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:08:52.791687Z","iopub.execute_input":"2021-12-31T07:08:52.792107Z","iopub.status.idle":"2021-12-31T07:09:12.473778Z","shell.execute_reply.started":"2021-12-31T07:08:52.792071Z","shell.execute_reply":"2021-12-31T07:09:12.473071Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 📦 MMDetetection\n<div align=center><img src=\"https://raw.githubusercontent.com/open-mmlab/mmdetection/master/resources/mmdet-logo.png\" width=500></div>\n\nHere are some cool facts about **MMDet**,\n* It comes with bunch of sota models. You may get tired trying them out ;)\n* Easy to comstomize and Deploy.\n* It has built-in **wandb** integration. So, we can easily track our training.\n* Error/Result Analysis is easier.","metadata":{}},{"cell_type":"code","source":"# mmdet\n!rm -r /kaggle/working/mmdetection\n!cp -r /kaggle/input/mmdet-repo-ds /kaggle/working/mmdetection\n%cd mmdetection\n!pip install -q -e .","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:09:12.474819Z","iopub.execute_input":"2021-12-31T07:09:12.475095Z","iopub.status.idle":"2021-12-31T07:09:31.25666Z","shell.execute_reply.started":"2021-12-31T07:09:12.475057Z","shell.execute_reply":"2021-12-31T07:09:31.255803Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ⚙️ Configuration\n> You can tune following parameters for better result. \n\n* **Num Classes** \n* **Score-Theshold**\n* **IoU**\n* **Wandb**\n```\ndict(type='WandbLoggerHook', # this is where magic happens ;)\n     init_kwargs=dict(project='sartorius',\n                      name=f'mask_rcnn_r50',\n                      config={'config':mask_rcnn_r50_fpn_1x_coco,\n                      'comment':'baseline01',},\n                      entity=None)) # this is where magic happens\n```\n* **Augmentation** \n    * **Flip**\n    * **Multi-Scale**\n    * **PhotoMetricDistortion**\n* **Batch Size**","metadata":{}},{"cell_type":"code","source":"!mkdir -p configs/sartorius","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:09:31.260195Z","iopub.execute_input":"2021-12-31T07:09:31.260409Z","iopub.status.idle":"2021-12-31T07:09:31.927825Z","shell.execute_reply.started":"2021-12-31T07:09:31.260382Z","shell.execute_reply":"2021-12-31T07:09:31.926914Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile configs/sartorius/custom_config.py\n\n# model settings\nmodel = dict(\n    type='MaskRCNN',\n    backbone=dict(\n        type='ResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=1,\n        norm_cfg=dict(type='BN', requires_grad=True),\n        norm_eval=True,\n        style='pytorch',\n        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n    neck=dict(\n        type='FPN',\n        in_channels=[256, 512, 1024, 2048],\n        out_channels=256,\n        num_outs=5),\n    rpn_head=dict(\n        type='RPNHead',\n        in_channels=256,\n        feat_channels=256,\n        anchor_generator=dict(\n            type='AnchorGenerator',\n            scales=[8],\n            ratios=[0.5, 1.0, 2.0],\n            strides=[4, 8, 16, 32, 64]),\n        bbox_coder=dict(\n            type='DeltaXYWHBBoxCoder',\n            target_means=[.0, .0, .0, .0],\n            target_stds=[1.0, 1.0, 1.0, 1.0]),\n        loss_cls=dict(\n            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n    roi_head=dict(\n        type='StandardRoIHead',\n        bbox_roi_extractor=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        bbox_head=dict(\n            type='Shared2FCBBoxHead',\n            in_channels=256,\n            fc_out_channels=1024,\n            roi_feat_size=7,\n            num_classes=1,\n            bbox_coder=dict(\n                type='DeltaXYWHBBoxCoder',\n                target_means=[0., 0., 0., 0.],\n                target_stds=[0.1, 0.1, 0.2, 0.2]),\n            reg_class_agnostic=False,\n            loss_cls=dict(\n                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n        mask_roi_extractor=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        mask_head=dict(\n            type='FCNMaskHead',\n            num_convs=4,\n            in_channels=256,\n            conv_out_channels=256,\n            num_classes=1,\n            loss_mask=dict(\n                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\n    # model training and testing settings\n    train_cfg=dict(\n        rpn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.7,\n                neg_iou_thr=0.3,\n                min_pos_iou=0.3,\n                match_low_quality=True,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=256,\n                pos_fraction=0.5,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=False),\n            allowed_border=-1,\n            pos_weight=-1,\n            debug=False),\n        rpn_proposal=dict(\n            nms_pre=2000,\n            max_per_img=1000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.5,\n                neg_iou_thr=0.5,\n                min_pos_iou=0.5,\n                match_low_quality=True,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=512,\n                pos_fraction=0.25,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=True),\n            mask_size=28,\n            pos_weight=-1,\n            debug=False)),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=1000,\n            max_per_img=1000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            score_thr=0.05,\n            nms=dict(type='nms', iou_threshold=0.5),\n            max_per_img=100,\n            mask_thr_binary=0.5)))\n\n# dataset settings\ndataset_type = 'CocoDataset'\nclasses = ('cell',) # Added\ndata_root = '/kaggle/input/sartorius-coco-dataset/' # Modified\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n    dict(type='Resize',\n         img_scale=(768, 768), # [(1280, 1280), (1152, 1152), (1024, 1024)],\n#          multiscale_mode='value',\n         keep_ratio=True),\n    dict(type='RandomFlip', direction=['horizontal', 'vertical'], flip_ratio=0.5), # augmentation starts\n    dict(type='PhotoMetricDistortion',\n         brightness_delta=32, contrast_range=(0.5, 1.5),\n         saturation_range=(0.5, 1.5), hue_delta=18),\n    dict(type='Normalize', **img_norm_cfg),\n    dict(type='Pad', size_divisor=32),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(768, 768), # (1280, 1280),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\n            dict(type='Normalize', **img_norm_cfg),\n            dict(type='Pad', size_divisor=32),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img']),\n        ])\n]\ndata = dict(\n    samples_per_gpu=12, # BATCH_SIZE\n    workers_per_gpu=2,\n    train=dict(\n        type=dataset_type,\n        ann_file=data_root + 'annotations_train.json', # Modified\n        img_prefix=data_root + 'train2017/', # Modified\n        classes=classes, # Added\n        pipeline=train_pipeline),\n    val=dict(\n        type=dataset_type,\n        ann_file=data_root + 'annotations_valid.json', # Modified\n        img_prefix=data_root + 'valid2017/', # Modified\n        classes=classes, # Added\n        pipeline=test_pipeline),\n    test=dict(\n        type=dataset_type,\n        ann_file=data_root + 'annotations_valid.json', # Modified\n        img_prefix=data_root + 'valid2017/', # Modified\n        classes=classes, # Added\n        pipeline=test_pipeline))\nevaluation = dict(interval=1,\n                  metric=['bbox','segm'], # bbox, segm\n                  save_best='segm_mAP')\n\n\n\n# optimizer\noptimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=None)\n# learning policy\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.001,\n    step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=15)\n\n# default_runtime\ncheckpoint_config = dict(interval=-1)\n# yapf:disable\nlog_config = dict(\n    interval=10,\n    hooks=[\n        dict(type='TextLoggerHook'),\n        dict(type='WandbLoggerHook', # wandb logger\n             init_kwargs=dict(project='sartorius-public',\n                              name=f'mask_rcnn-resnet50-768x768-fold0',\n                              config={'config':'mask_rcnn_r50_fpn_1x_coco',\n                                      'exp_name':'mask_rcnn-resnet50-768x768',\n                                      'comment':'baseline',\n                                      'batch_size':12,\n                                      'lr':0.020\n                                     },\n                              group='exp_name',\n                              entity=None))\n        # dict(type='TensorboardLoggerHook')\n    ])\n# yapf:enable\ncustom_hooks = [dict(type='NumClassCheckHook')]\n\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth'\nresume_from = None\nworkflow = [('train', 1)]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-31T07:17:16.074985Z","iopub.execute_input":"2021-12-31T07:17:16.075311Z","iopub.status.idle":"2021-12-31T07:17:16.085699Z","shell.execute_reply.started":"2021-12-31T07:17:16.07528Z","shell.execute_reply":"2021-12-31T07:17:16.084989Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🚅 Training","metadata":{}},{"cell_type":"code","source":"!python tools/train.py {config}","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-12-31T07:17:19.973414Z","iopub.execute_input":"2021-12-31T07:17:19.974194Z","iopub.status.idle":"2021-12-31T07:45:21.116256Z","shell.execute_reply.started":"2021-12-31T07:17:19.974157Z","shell.execute_reply":"2021-12-31T07:45:21.115384Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ls work_dirs/custom_config","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:45:26.107625Z","iopub.execute_input":"2021-12-31T07:45:26.107915Z","iopub.status.idle":"2021-12-31T07:45:26.790269Z","shell.execute_reply.started":"2021-12-31T07:45:26.107882Z","shell.execute_reply":"2021-12-31T07:45:26.789352Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ✨ 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"}}},{"cell_type":"markdown","source":"# 🔎 Result Analysis","metadata":{}},{"cell_type":"markdown","source":"## Best Checkpoint","metadata":{}},{"cell_type":"code","source":"ckpt  = glob('work_dirs/custom_config/best_segm_mAP_epoch_*.pth')[0]\nckpt","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:45:30.081775Z","iopub.execute_input":"2021-12-31T07:45:30.082572Z","iopub.status.idle":"2021-12-31T07:45:30.090816Z","shell.execute_reply.started":"2021-12-31T07:45:30.082531Z","shell.execute_reply":"2021-12-31T07:45:30.090015Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Test Model","metadata":{}},{"cell_type":"code","source":"!python tools/test.py {config} {ckpt} --eval segm --out /kaggle/working/result.pkl","metadata":{"_kg_hide-output":false,"execution":{"iopub.status.busy":"2021-12-31T07:45:31.507922Z","iopub.execute_input":"2021-12-31T07:45:31.508369Z","iopub.status.idle":"2021-12-31T07:47:22.270477Z","shell.execute_reply.started":"2021-12-31T07:45:31.508332Z","shell.execute_reply":"2021-12-31T07:47:22.269599Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Analyze","metadata":{}},{"cell_type":"code","source":"!python tools/analysis_tools/analyze_results.py \\\n       {config} \\\n       /kaggle/working/result.pkl \\\n       /kaggle/working/result\\\n       --show-score-thr 0.40","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:48:24.411251Z","iopub.execute_input":"2021-12-31T07:48:24.412066Z","iopub.status.idle":"2021-12-31T07:50:58.485431Z","shell.execute_reply.started":"2021-12-31T07:48:24.412018Z","shell.execute_reply":"2021-12-31T07:50:58.484531Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Utils","metadata":{}},{"cell_type":"code","source":"def plot_batch(paths,row = 3, col = 2, scale=1.5):\n    plt.figure(figsize=(col*7.04*scale, row*5.2*scale))\n    for i, path in enumerate(paths[:row*col]):\n        plt.subplot(row, col, i+1)\n        img = cv2.imread(path)[...,::-1]\n        plt.imshow(img)\n    plt.tight_layout()\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:53:27.0628Z","iopub.execute_input":"2021-12-31T07:53:27.063126Z","iopub.status.idle":"2021-12-31T07:53:27.069783Z","shell.execute_reply.started":"2021-12-31T07:53:27.063092Z","shell.execute_reply":"2021-12-31T07:53:27.069063Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 👍 Good Ones","metadata":{}},{"cell_type":"code","source":"good_paths = glob('/kaggle/working/result/good/*')\nplot_batch(good_paths)","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:53:28.681588Z","iopub.execute_input":"2021-12-31T07:53:28.681845Z","iopub.status.idle":"2021-12-31T07:53:30.622843Z","shell.execute_reply.started":"2021-12-31T07:53:28.681815Z","shell.execute_reply":"2021-12-31T07:53:30.620127Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 👎 Bad Ones","metadata":{}},{"cell_type":"code","source":"bad_paths = glob('/kaggle/working/result/bad/*')\nplot_batch(bad_paths)","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:53:30.624517Z","iopub.execute_input":"2021-12-31T07:53:30.62541Z","iopub.status.idle":"2021-12-31T07:53:32.84768Z","shell.execute_reply.started":"2021-12-31T07:53:30.625372Z","shell.execute_reply":"2021-12-31T07:53:32.84412Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ✂️ Remove Files","metadata":{}},{"cell_type":"code","source":"!cp -r work_dirs /kaggle/working\n%cd /kaggle/working\n!rm -r /kaggle/working/mmdetection","metadata":{"execution":{"iopub.status.busy":"2021-12-31T07:56:40.583871Z","iopub.execute_input":"2021-12-31T07:56:40.58417Z","iopub.status.idle":"2021-12-31T07:56:44.324957Z","shell.execute_reply.started":"2021-12-31T07:56:40.584138Z","shell.execute_reply":"2021-12-31T07:56:44.32366Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 💡 Reference\n* [mmdetection for segmentation [training]](https://www.kaggle.com/its7171/mmdetection-for-segmentation-training) by @its7171\n* [Sartorius Segmentation - Mask Dataset](https://www.kaggle.com/dschettler8845/sartorius-segmentation-mask-dataset) by @dschettler8845","metadata":{}},{"cell_type":"markdown","source":"# Please Upvote If you find this notebook Useful :)","metadata":{}}]}