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## About code isolation |
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This `downstream_mmdet` is isolated from pre-training codes. One can treat this `downstream_mmdet` as an independent codebase 🛠️. |
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## Fine-tuned ConvNeXt-B weights, log files, and performance |
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<div align="center"> |
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[[`weights (pre-trained by SparK)`](https://drive.google.com/file/d/1ZjWbqI1qoBcqeQijI5xX9E-YNkxpJcYV/view?usp=share_link)] |
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[[`weights (fine-tuned on COCO)`](https://drive.google.com/file/d/1t10dmzg5KOO27o2yIglK-gQepB5gR4zR/view?usp=share_link)] |
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[[`log.json`](https://drive.google.com/file/d/1TuNboXl1qwjf1tggZ3QOssI67uU7Jtig/view?usp=share_link)] |
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[[`log`](https://drive.google.com/file/d/1JY5CkL_MX08zJ8P1FBIeC60OJsuIiyZc/view?usp=sharing)] |
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</div> |
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<p align="center"> |
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<img src="https://user-images.githubusercontent.com/39692511/211497396-cd031318-ef54-45a4-a283-cd9810c15603.png" width=80%> |
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<p> |
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## Installation [MMDetection with commit 6a979e2](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/tree/6a979e2164e3fb0de0ca2546545013a4d71b2f7d) before fine-tuning ConvNeXt on COCO |
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We refer to the codebases of [ConvNeXt](https://github.com/facebookresearch/ConvNeXt/tree/048efcea897d999aed302f2639b6270aedf8d4c8) and [Swin-Transformer-Object-Detection](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/tree/6a979e2164e3fb0de0ca2546545013a4d71b2f7d). |
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Please refer to [README.md](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/6a979e2164e3fb0de0ca2546545013a4d71b2f7d/README.md) for installation and dataset preparation instructions. |
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Note the COCO dataset folder should be at `downstream_mmdet/data/coco`. |
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The folder should follow the directory structure requried by `MMDetection`, which should look like this: |
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``` |
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downstream_mmdet/data/coco: |
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annotations/: |
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captions_train2017.json captions_val2017.json |
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instances_train2017.json instances_val2017.json |
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person_keypoints_train2017.json person_keypoints_val2017.json |
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train2017/: |
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a_lot_images.jpg |
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val2017/: |
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a_lot_images.jpg |
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``` |
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### Training |
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To train a detector with pre-trained models, run: |
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``` |
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# single-gpu training |
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python tools/train.py <CONFIG_FILE> --cfg-options model.pretrained=<PRETRAIN_MODEL> [other optional arguments] |
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# multi-gpu training |
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tools/dist_train.sh <CONFIG_FILE> <GPU_NUM> --cfg-options model.pretrained=<PRETRAIN_MODEL> [other optional arguments] |
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``` |
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For example, to train a Mask R-CNN model with a SparK pretrained `ConvNeXt-B` backbone and 4 gpus, run: |
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``` |
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tools/dist_train.sh configs/convnext_spark/mask_rcnn_convnext_base_patch4_window7_mstrain_480-800_adamw_3x_coco_in1k.py 4 \ |
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--cfg-options model.pretrained=/some/path/to/official_convnext_base_1kpretrained.pth |
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``` |
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The Mask R-CNN 3x fine-tuning config file can be found at [`configs/convnext_spark`](configs/convnext_spark). This config is basically a copy of [https://github.com/facebookresearch/ConvNeXt/blob/main/object_detection/configs/convnext/mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco_in1k.py](https://github.com/facebookresearch/ConvNeXt/blob/main/object_detection/configs/convnext/mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco_in1k.py). |
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### Inference |
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``` |
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# single-gpu testing |
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python tools/test.py <CONFIG_FILE> <DET_CHECKPOINT_FILE> --eval bbox segm |
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# multi-gpu testing |
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tools/dist_test.sh <CONFIG_FILE> <DET_CHECKPOINT_FILE> <GPU_NUM> --eval bbox segm |
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``` |
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## Acknowledgment |
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We appreciate these useful codebases: |
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- [MMDetection](https://github.com/open-mmlab/mmdetection) |
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- [ConvNeXt](https://github.com/facebookresearch/ConvNeXt) |
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- [Swin-Transformer-Object-Detection](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection) |
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