ZJF-Thunder
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Browse files- checkpoints/epoch_200.pth +3 -0
- checkpoints/epoch_50.pth +3 -0
- checkpoints/mask_rcnn_swin_tiny_patch4_window7.pth +3 -0
- mask_rcnn_swin_tiny_patch4_window7.pth +3 -0
- mmdet.egg-info/PKG-INFO +167 -0
- mmdet.egg-info/SOURCES.txt +978 -0
- mmdet.egg-info/dependency_links.txt +1 -0
- mmdet.egg-info/not-zip-safe +1 -0
- mmdet.egg-info/requires.txt +59 -0
- mmdet.egg-info/top_level.txt +2 -0
- work_dirs/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco/epoch_200.pth +3 -0
- work_dirs/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco/latest.pth +3 -0
- work_dirs/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py +300 -0
checkpoints/epoch_200.pth
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checkpoints/epoch_50.pth
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checkpoints/mask_rcnn_swin_tiny_patch4_window7.pth
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mask_rcnn_swin_tiny_patch4_window7.pth
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mmdet.egg-info/PKG-INFO
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+
Metadata-Version: 2.1
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Name: mmdet
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Version: 2.11.0
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Summary: OpenMMLab Detection Toolbox and Benchmark
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Home-page: https://github.com/open-mmlab/mmdetection
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Author: OpenMMLab
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Author-email: [email protected]
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License: Apache License 2.0
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Keywords: computer vision,object detection
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Platform: UNKNOWN
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: License :: OSI Approved :: Apache Software License
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+
Classifier: Operating System :: OS Independent
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.6
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Classifier: Programming Language :: Python :: 3.7
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Classifier: Programming Language :: Python :: 3.8
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Description-Content-Type: text/markdown
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Provides-Extra: all
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Provides-Extra: tests
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Provides-Extra: build
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Provides-Extra: optional
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License-File: LICENSE
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+
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# Swin Transformer for Object Detection
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This repo contains the supported code and configuration files to reproduce object detection results of [Swin Transformer](https://arxiv.org/pdf/2103.14030.pdf). It is based on [mmdetection](https://github.com/open-mmlab/mmdetection).
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## Updates
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***05/11/2021*** Models for [MoBY](https://github.com/SwinTransformer/Transformer-SSL) are released
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***04/12/2021*** Initial commits
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## Results and Models
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### Mask R-CNN
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| 38 |
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| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
|
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| Swin-T | ImageNet-1K | 1x | 43.7 | 39.8 | 48M | 267G | [config](configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/mask_rcnn_swin_tiny_patch4_window7_1x.log.json)/[baidu](https://pan.baidu.com/s/1bYZk7BIeFEozjRNUesxVWg) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/mask_rcnn_swin_tiny_patch4_window7_1x.pth)/[baidu](https://pan.baidu.com/s/19UOW0xl0qc-pXQ59aFKU5w) |
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| Swin-T | ImageNet-1K | 3x | 46.0 | 41.6 | 48M | 267G | [config](configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_tiny_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/1Te-Ovk4yaavmE4jcIOPAaw) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_tiny_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1YpauXYAFOohyMi3Vkb6DBg) |
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| Swin-S | ImageNet-1K | 3x | 48.5 | 43.3 | 69M | 359G | [config](configs/swin/mask_rcnn_swin_small_patch4_window7_mstrain_480-800_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_small_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/1ymCK7378QS91yWlxHMf1yw) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_small_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1V4w4aaV7HSjXNFTOSA6v6w) |
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### Cascade Mask R-CNN
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| 46 |
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| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
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| Swin-T | ImageNet-1K | 1x | 48.1 | 41.7 | 86M | 745G | [config](configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_1x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/cascade_mask_rcnn_swin_tiny_patch4_window7_1x.log.json)/[baidu](https://pan.baidu.com/s/1x4vnorYZfISr-d_VUSVQCA) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/cascade_mask_rcnn_swin_tiny_patch4_window7_1x.pth)/[baidu](https://pan.baidu.com/s/1vFwbN1iamrtwnQSxMIW4BA) |
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| Swin-T | ImageNet-1K | 3x | 50.4 | 43.7 | 86M | 745G | [config](configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_tiny_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/1GW_ic617Ak_NpRayOqPSOA) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_tiny_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1i-izBrODgQmMwTv6F6-x3A) |
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| Swin-S | ImageNet-1K | 3x | 51.9 | 45.0 | 107M | 838G | [config](configs/swin/cascade_mask_rcnn_swin_small_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_small_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/17Vyufk85vyocxrBT1AbavQ) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_small_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1Sv9-gP1Qpl6SGOF6DBhUbw) |
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| Swin-B | ImageNet-1K | 3x | 51.9 | 45.0 | 145M | 982G | [config](configs/swin/cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_base_patch4_window7.log.json)/[baidu](https://pan.baidu.com/s/1UZAR39g-0kE_aGrINwfVHg) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.2/cascade_mask_rcnn_swin_base_patch4_window7.pth)/[baidu](https://pan.baidu.com/s/1tHoC9PMVnldQUAfcF6FT3A) |
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### RepPoints V2
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| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| Swin-T | ImageNet-1K | 3x | 50.0 | - | 45M | 283G |
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### Mask RepPoints V2
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| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| Swin-T | ImageNet-1K | 3x | 50.3 | 43.6 | 47M | 292G |
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**Notes**:
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- **Pre-trained models can be downloaded from [Swin Transformer for ImageNet Classification](https://github.com/microsoft/Swin-Transformer)**.
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- Access code for `baidu` is `swin`.
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## Results of MoBY with Swin Transformer
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### Mask R-CNN
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| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
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| Swin-T | ImageNet-1K | 1x | 43.6 | 39.6 | 48M | 267G | [config](configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_mask_rcnn_swin_tiny_patch4_window7_1x.log.json)/[baidu](https://pan.baidu.com/s/1P5gCIfLUQ64jbVMOom0H3w) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_mask_rcnn_swin_tiny_patch4_window7_1x.pth)/[baidu](https://pan.baidu.com/s/1xGRihuIrGVreFKn5eJ6oTg) |
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| Swin-T | ImageNet-1K | 3x | 46.0 | 41.7 | 48M | 267G | [config](configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_mask_rcnn_swin_tiny_patch4_window7_3x.log.json)/[baidu](https://pan.baidu.com/s/17WAhUmhAam1of3hXOu-wtA) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_mask_rcnn_swin_tiny_patch4_window7_3x.pth)/[baidu](https://pan.baidu.com/s/1MSj8cC1wlQU1QaXCdKrzeA) |
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### Cascade Mask R-CNN
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| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |:---: |
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| Swin-T | ImageNet-1K | 1x | 48.1 | 41.5 | 86M | 745G | [config](configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_1x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_cascade_mask_rcnn_swin_tiny_patch4_window7_1x.log.json)/[baidu](https://pan.baidu.com/s/1eOdq1rvi0QoXjc7COgiM7A) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_cascade_mask_rcnn_swin_tiny_patch4_window7_1x.pth)/[baidu](https://pan.baidu.com/s/1-gbY-LExbf0FgYxWWs8OPg) |
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| Swin-T | ImageNet-1K | 3x | 50.2 | 43.5 | 86M | 745G | [config](configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_cascade_mask_rcnn_swin_tiny_patch4_window7_3x.log.json)/[baidu](https://pan.baidu.com/s/1zEFXHYjEiXUCWF1U7HR5Zg) | [github](https://github.com/SwinTransformer/storage/releases/download/v1.0.3/moby_cascade_mask_rcnn_swin_tiny_patch4_window7_3x.pth)/[baidu](https://pan.baidu.com/s/1FMmW0GOpT4MKsKUrkJRgeg) |
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**Notes:**
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- The drop path rate needs to be tuned for best practice.
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- MoBY pre-trained models can be downloaded from [MoBY with Swin Transformer](https://github.com/SwinTransformer/Transformer-SSL).
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## Usage
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### Installation
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Please refer to [get_started.md](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md) for installation and dataset preparation.
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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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### 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> [model.backbone.use_checkpoint=True] [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> [model.backbone.use_checkpoint=True] [other optional arguments]
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```
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For example, to train a Cascade Mask R-CNN model with a `Swin-T` backbone and 8 gpus, run:
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```
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| 119 |
+
tools/dist_train.sh configs/swin/cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py 8 --cfg-options model.pretrained=<PRETRAIN_MODEL>
|
| 120 |
+
```
|
| 121 |
+
|
| 122 |
+
**Note:** `use_checkpoint` is used to save GPU memory. Please refer to [this page](https://pytorch.org/docs/stable/checkpoint.html) for more details.
|
| 123 |
+
|
| 124 |
+
|
| 125 |
+
### Apex (optional):
|
| 126 |
+
We use apex for mixed precision training by default. To install apex, run:
|
| 127 |
+
```
|
| 128 |
+
git clone https://github.com/NVIDIA/apex
|
| 129 |
+
cd apex
|
| 130 |
+
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
|
| 131 |
+
```
|
| 132 |
+
If you would like to disable apex, modify the type of runner as `EpochBasedRunner` and comment out the following code block in the [configuration files](configs/swin):
|
| 133 |
+
```
|
| 134 |
+
# do not use mmdet version fp16
|
| 135 |
+
fp16 = None
|
| 136 |
+
optimizer_config = dict(
|
| 137 |
+
type="DistOptimizerHook",
|
| 138 |
+
update_interval=1,
|
| 139 |
+
grad_clip=None,
|
| 140 |
+
coalesce=True,
|
| 141 |
+
bucket_size_mb=-1,
|
| 142 |
+
use_fp16=True,
|
| 143 |
+
)
|
| 144 |
+
```
|
| 145 |
+
|
| 146 |
+
## Citing Swin Transformer
|
| 147 |
+
```
|
| 148 |
+
@article{liu2021Swin,
|
| 149 |
+
title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
|
| 150 |
+
author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
|
| 151 |
+
journal={arXiv preprint arXiv:2103.14030},
|
| 152 |
+
year={2021}
|
| 153 |
+
}
|
| 154 |
+
```
|
| 155 |
+
|
| 156 |
+
## Other Links
|
| 157 |
+
|
| 158 |
+
> **Image Classification**: See [Swin Transformer for Image Classification](https://github.com/microsoft/Swin-Transformer).
|
| 159 |
+
|
| 160 |
+
> **Semantic Segmentation**: See [Swin Transformer for Semantic Segmentation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation).
|
| 161 |
+
|
| 162 |
+
> **Self-Supervised Learning**: See [MoBY with Swin Transformer](https://github.com/SwinTransformer/Transformer-SSL).
|
| 163 |
+
|
| 164 |
+
> **Video Recognition**, See [Video Swin Transformer](https://github.com/SwinTransformer/Video-Swin-Transformer).
|
| 165 |
+
|
| 166 |
+
|
| 167 |
+
|
mmdet.egg-info/SOURCES.txt
ADDED
|
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.gitignore
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.pre-commit-config.yaml
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.readthedocs.yml
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LICENSE
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README.md
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pytest.ini
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requirements.txt
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setup.cfg
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setup.py
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.dev_scripts/batch_test.py
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.dev_scripts/batch_test.sh
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.dev_scripts/benchmark_filter.py
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.dev_scripts/convert_benchmark_script.py
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.dev_scripts/gather_benchmark_metric.py
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.dev_scripts/gather_models.py
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.dev_scripts/linter.sh
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.github/CODE_OF_CONDUCT.md
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.github/CONTRIBUTING.md
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.github/ISSUE_TEMPLATE/config.yml
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.github/ISSUE_TEMPLATE/error-report.md
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.github/ISSUE_TEMPLATE/feature_request.md
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.github/ISSUE_TEMPLATE/general_questions.md
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.github/ISSUE_TEMPLATE/reimplementation_questions.md
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.github/workflows/build.yml
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.github/workflows/build_pat.yml
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.github/workflows/deploy.yml
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configs/_base_/default_runtime.py
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configs/_base_/datasets/cityscapes_detection.py
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configs/_base_/datasets/cityscapes_instance.py
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configs/_base_/datasets/coco_detection.py
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configs/_base_/datasets/coco_instance.py
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configs/_base_/datasets/coco_instance_semantic.py
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configs/_base_/datasets/deepfashion.py
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configs/_base_/datasets/lvis_v0.5_instance.py
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configs/_base_/datasets/lvis_v1_instance.py
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configs/_base_/datasets/voc0712.py
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configs/_base_/datasets/wider_face.py
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configs/_base_/models/cascade_mask_rcnn_r50_fpn.py
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configs/_base_/models/cascade_mask_rcnn_swin_fpn.py
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configs/_base_/models/cascade_rcnn_r50_fpn.py
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configs/_base_/models/fast_rcnn_r50_fpn.py
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configs/_base_/models/faster_rcnn_r50_caffe_c4.py
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| 43 |
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configs/_base_/models/faster_rcnn_r50_caffe_dc5.py
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| 44 |
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configs/_base_/models/faster_rcnn_r50_fpn.py
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| 45 |
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configs/_base_/models/mask_rcnn_r50_caffe_c4.py
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configs/_base_/models/mask_rcnn_r50_fpn.py
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| 47 |
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configs/_base_/models/mask_rcnn_swin_fpn.py
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| 48 |
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configs/_base_/models/retinanet_r50_fpn.py
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| 49 |
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configs/_base_/models/rpn_r50_caffe_c4.py
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configs/_base_/models/rpn_r50_fpn.py
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| 51 |
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configs/_base_/models/ssd300.py
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configs/_base_/schedules/schedule_1x.py
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configs/_base_/schedules/schedule_20e.py
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| 54 |
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configs/_base_/schedules/schedule_2x.py
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| 55 |
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configs/albu_example/README.md
|
| 56 |
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configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py
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| 57 |
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configs/atss/README.md
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| 58 |
+
configs/atss/atss_r101_fpn_1x_coco.py
|
| 59 |
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configs/atss/atss_r50_fpn_1x_coco.py
|
| 60 |
+
configs/carafe/README.md
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| 61 |
+
configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py
|
| 62 |
+
configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py
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| 63 |
+
configs/cascade_rcnn/README.md
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| 64 |
+
configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
|
| 65 |
+
configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py
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| 66 |
+
configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
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| 67 |
+
configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
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| 68 |
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configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
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| 69 |
+
configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py
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| 70 |
+
configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
|
| 71 |
+
configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
|
| 72 |
+
configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
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| 73 |
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configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
|
| 74 |
+
configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py
|
| 75 |
+
configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py
|
| 76 |
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configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
|
| 77 |
+
configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
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| 78 |
+
configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
|
| 79 |
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configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
|
| 80 |
+
configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py
|
| 81 |
+
configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py
|
| 82 |
+
configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py
|
| 83 |
+
configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py
|
| 84 |
+
configs/cascade_rpn/README.md
|
| 85 |
+
configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py
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| 86 |
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configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
|
| 87 |
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configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py
|
| 88 |
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configs/centripetalnet/README.md
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| 89 |
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configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py
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| 90 |
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configs/cityscapes/README.md
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| 91 |
+
configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py
|
| 92 |
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configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py
|
| 93 |
+
configs/cornernet/README.md
|
| 94 |
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configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py
|
| 95 |
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configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py
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| 96 |
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configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py
|
| 97 |
+
configs/dcn/README.md
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| 98 |
+
configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
|
| 99 |
+
configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
|
| 100 |
+
configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
|
| 101 |
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configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
|
| 102 |
+
configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
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| 103 |
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configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
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| 104 |
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configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
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| 105 |
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configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
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| 106 |
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configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
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| 107 |
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configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
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| 108 |
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configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py
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| 109 |
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configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
|
| 110 |
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configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
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| 111 |
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configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
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| 112 |
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configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
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| 113 |
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configs/deepfashion/README.md
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| 114 |
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configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py
|
| 115 |
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configs/detectors/README.md
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| 116 |
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configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py
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| 117 |
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configs/detectors/cascade_rcnn_r50_sac_1x_coco.py
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| 118 |
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configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py
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| 119 |
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configs/detectors/detectors_htc_r50_1x_coco.py
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| 120 |
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configs/detectors/htc_r50_rfp_1x_coco.py
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| 121 |
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configs/detectors/htc_r50_sac_1x_coco.py
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configs/detr/README.md
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| 123 |
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configs/detr/detr_r50_8x2_150e_coco.py
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configs/double_heads/README.md
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configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py
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configs/dynamic_rcnn/README.md
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configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py
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configs/empirical_attention/README.md
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| 129 |
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configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py
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| 130 |
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configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py
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| 131 |
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configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py
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| 132 |
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configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
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| 133 |
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configs/fast_rcnn/README.md
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| 134 |
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configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py
|
| 135 |
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configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py
|
| 136 |
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configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py
|
| 137 |
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configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py
|
| 138 |
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configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py
|
| 139 |
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configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py
|
| 140 |
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configs/faster_rcnn/README.md
|
| 141 |
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configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py
|
| 142 |
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configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py
|
| 143 |
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configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py
|
| 144 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py
|
| 145 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py
|
| 146 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py
|
| 147 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
|
| 148 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py
|
| 149 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py
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| 150 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py
|
| 151 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
|
| 152 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py
|
| 153 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
|
| 154 |
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configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py
|
| 155 |
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configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py
|
| 156 |
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configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py
|
| 157 |
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configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py
|
| 158 |
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configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py
|
| 159 |
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configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py
|
| 160 |
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configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py
|
| 161 |
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configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py
|
| 162 |
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configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py
|
| 163 |
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configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py
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| 164 |
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configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py
|
| 165 |
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configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py
|
| 166 |
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configs/fcos/README.md
|
| 167 |
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configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py
|
| 168 |
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configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
|
| 169 |
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configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py
|
| 170 |
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configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py
|
| 171 |
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configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
|
| 172 |
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configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py
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| 173 |
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configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py
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| 174 |
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configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
|
| 175 |
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configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
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configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py
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configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py
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configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py
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configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py
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configs/reppoints/reppoints.png
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configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py
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configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py
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configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
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configs/res2net/htc_r2_101_fpn_20e_coco.py
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configs/resnest/README.md
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configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
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configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
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configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
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configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py
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configs/tridentnet/tridentnet_r50_caffe_1x_coco.py
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docker/Dockerfile
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docker/serve/Dockerfile
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docker/serve/config.properties
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resources/corruptions_sev_3.png
|
| 872 |
+
resources/data_pipeline.png
|
| 873 |
+
resources/loss_curve.png
|
| 874 |
+
resources/mmdet-logo.png
|
| 875 |
+
tests/data/coco_sample.json
|
| 876 |
+
tests/data/color.jpg
|
| 877 |
+
tests/data/gray.jpg
|
| 878 |
+
tests/data/VOCdevkit/VOC2007/Annotations/000001.xml
|
| 879 |
+
tests/data/VOCdevkit/VOC2007/ImageSets/Main/test.txt
|
| 880 |
+
tests/data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt
|
| 881 |
+
tests/data/VOCdevkit/VOC2007/JPEGImages/000001.jpg
|
| 882 |
+
tests/data/VOCdevkit/VOC2012/Annotations/000001.xml
|
| 883 |
+
tests/data/VOCdevkit/VOC2012/ImageSets/Main/test.txt
|
| 884 |
+
tests/data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt
|
| 885 |
+
tests/data/VOCdevkit/VOC2012/JPEGImages/000001.jpg
|
| 886 |
+
tests/test_data/test_utils.py
|
| 887 |
+
tests/test_data/test_datasets/test_coco_dataset.py
|
| 888 |
+
tests/test_data/test_datasets/test_common.py
|
| 889 |
+
tests/test_data/test_datasets/test_custom_dataset.py
|
| 890 |
+
tests/test_data/test_datasets/test_dataset_wrapper.py
|
| 891 |
+
tests/test_data/test_datasets/test_xml_dataset.py
|
| 892 |
+
tests/test_data/test_pipelines/test_formatting.py
|
| 893 |
+
tests/test_data/test_pipelines/test_loading.py
|
| 894 |
+
tests/test_data/test_pipelines/test_sampler.py
|
| 895 |
+
tests/test_data/test_pipelines/test_transform/test_img_augment.py
|
| 896 |
+
tests/test_data/test_pipelines/test_transform/test_models_aug_test.py
|
| 897 |
+
tests/test_data/test_pipelines/test_transform/test_rotate.py
|
| 898 |
+
tests/test_data/test_pipelines/test_transform/test_shear.py
|
| 899 |
+
tests/test_data/test_pipelines/test_transform/test_transform.py
|
| 900 |
+
tests/test_data/test_pipelines/test_transform/test_translate.py
|
| 901 |
+
tests/test_metrics/test_box_overlap.py
|
| 902 |
+
tests/test_metrics/test_losses.py
|
| 903 |
+
tests/test_models/test_forward.py
|
| 904 |
+
tests/test_models/test_necks.py
|
| 905 |
+
tests/test_models/test_backbones/__init__.py
|
| 906 |
+
tests/test_models/test_backbones/test_hourglass.py
|
| 907 |
+
tests/test_models/test_backbones/test_regnet.py
|
| 908 |
+
tests/test_models/test_backbones/test_renext.py
|
| 909 |
+
tests/test_models/test_backbones/test_res2net.py
|
| 910 |
+
tests/test_models/test_backbones/test_resnest.py
|
| 911 |
+
tests/test_models/test_backbones/test_resnet.py
|
| 912 |
+
tests/test_models/test_backbones/test_trident_resnet.py
|
| 913 |
+
tests/test_models/test_backbones/utils.py
|
| 914 |
+
tests/test_models/test_dense_heads/test_anchor_head.py
|
| 915 |
+
tests/test_models/test_dense_heads/test_corner_head.py
|
| 916 |
+
tests/test_models/test_dense_heads/test_fcos_head.py
|
| 917 |
+
tests/test_models/test_dense_heads/test_fsaf_head.py
|
| 918 |
+
tests/test_models/test_dense_heads/test_ga_anchor_head.py
|
| 919 |
+
tests/test_models/test_dense_heads/test_ld_head.py
|
| 920 |
+
tests/test_models/test_dense_heads/test_paa_head.py
|
| 921 |
+
tests/test_models/test_dense_heads/test_pisa_head.py
|
| 922 |
+
tests/test_models/test_dense_heads/test_sabl_retina_head.py
|
| 923 |
+
tests/test_models/test_dense_heads/test_transformer_head.py
|
| 924 |
+
tests/test_models/test_dense_heads/test_vfnet_head.py
|
| 925 |
+
tests/test_models/test_dense_heads/test_yolact_head.py
|
| 926 |
+
tests/test_models/test_roi_heads/__init__.py
|
| 927 |
+
tests/test_models/test_roi_heads/test_bbox_head.py
|
| 928 |
+
tests/test_models/test_roi_heads/test_mask_head.py
|
| 929 |
+
tests/test_models/test_roi_heads/test_roi_extractor.py
|
| 930 |
+
tests/test_models/test_roi_heads/test_sabl_bbox_head.py
|
| 931 |
+
tests/test_models/test_roi_heads/utils.py
|
| 932 |
+
tests/test_models/test_utils/test_position_encoding.py
|
| 933 |
+
tests/test_models/test_utils/test_transformer.py
|
| 934 |
+
tests/test_onnx/__init__.py
|
| 935 |
+
tests/test_onnx/test_head.py
|
| 936 |
+
tests/test_onnx/test_neck.py
|
| 937 |
+
tests/test_onnx/utils.py
|
| 938 |
+
tests/test_onnx/data/retina_head_get_bboxes.pkl
|
| 939 |
+
tests/test_onnx/data/yolov3_head_get_bboxes.pkl
|
| 940 |
+
tests/test_onnx/data/yolov3_neck.pkl
|
| 941 |
+
tests/test_runtime/async_benchmark.py
|
| 942 |
+
tests/test_runtime/test_async.py
|
| 943 |
+
tests/test_runtime/test_config.py
|
| 944 |
+
tests/test_runtime/test_eval_hook.py
|
| 945 |
+
tests/test_runtime/test_fp16.py
|
| 946 |
+
tests/test_utils/test_anchor.py
|
| 947 |
+
tests/test_utils/test_assigner.py
|
| 948 |
+
tests/test_utils/test_coder.py
|
| 949 |
+
tests/test_utils/test_masks.py
|
| 950 |
+
tests/test_utils/test_misc.py
|
| 951 |
+
tests/test_utils/test_version.py
|
| 952 |
+
tests/test_utils/test_visualization.py
|
| 953 |
+
tools/dist_test.sh
|
| 954 |
+
tools/dist_train.sh
|
| 955 |
+
tools/slurm_test.sh
|
| 956 |
+
tools/slurm_train.sh
|
| 957 |
+
tools/test.py
|
| 958 |
+
tools/train.py
|
| 959 |
+
tools/analysis_tools/analyze_logs.py
|
| 960 |
+
tools/analysis_tools/analyze_results.py
|
| 961 |
+
tools/analysis_tools/benchmark.py
|
| 962 |
+
tools/analysis_tools/coco_error_analysis.py
|
| 963 |
+
tools/analysis_tools/eval_metric.py
|
| 964 |
+
tools/analysis_tools/get_flops.py
|
| 965 |
+
tools/analysis_tools/robustness_eval.py
|
| 966 |
+
tools/analysis_tools/test_robustness.py
|
| 967 |
+
tools/dataset_converters/cityscapes.py
|
| 968 |
+
tools/dataset_converters/pascal_voc.py
|
| 969 |
+
tools/deployment/mmdet2torchserve.py
|
| 970 |
+
tools/deployment/mmdet_handler.py
|
| 971 |
+
tools/deployment/onnx2tensorrt.py
|
| 972 |
+
tools/deployment/pytorch2onnx.py
|
| 973 |
+
tools/misc/browse_dataset.py
|
| 974 |
+
tools/misc/print_config.py
|
| 975 |
+
tools/model_converters/detectron2pytorch.py
|
| 976 |
+
tools/model_converters/publish_model.py
|
| 977 |
+
tools/model_converters/regnet2mmdet.py
|
| 978 |
+
tools/model_converters/upgrade_model_version.py
|
mmdet.egg-info/dependency_links.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
|
mmdet.egg-info/not-zip-safe
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
|
mmdet.egg-info/requires.txt
ADDED
|
@@ -0,0 +1,59 @@
|
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|
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|
|
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|
|
|
|
|
| 1 |
+
matplotlib
|
| 2 |
+
mmpycocotools
|
| 3 |
+
numpy
|
| 4 |
+
six
|
| 5 |
+
terminaltables
|
| 6 |
+
timm
|
| 7 |
+
|
| 8 |
+
[all]
|
| 9 |
+
cython
|
| 10 |
+
numpy
|
| 11 |
+
albumentations>=0.3.2
|
| 12 |
+
cityscapesscripts
|
| 13 |
+
imagecorruptions
|
| 14 |
+
mmlvis
|
| 15 |
+
scipy
|
| 16 |
+
sklearn
|
| 17 |
+
matplotlib
|
| 18 |
+
mmpycocotools
|
| 19 |
+
six
|
| 20 |
+
terminaltables
|
| 21 |
+
timm
|
| 22 |
+
asynctest
|
| 23 |
+
codecov
|
| 24 |
+
flake8
|
| 25 |
+
interrogate
|
| 26 |
+
isort==4.3.21
|
| 27 |
+
kwarray
|
| 28 |
+
onnx==1.7.0
|
| 29 |
+
onnxruntime==1.5.1
|
| 30 |
+
pytest
|
| 31 |
+
ubelt
|
| 32 |
+
xdoctest>=0.10.0
|
| 33 |
+
yapf
|
| 34 |
+
|
| 35 |
+
[build]
|
| 36 |
+
cython
|
| 37 |
+
numpy
|
| 38 |
+
|
| 39 |
+
[optional]
|
| 40 |
+
albumentations>=0.3.2
|
| 41 |
+
cityscapesscripts
|
| 42 |
+
imagecorruptions
|
| 43 |
+
mmlvis
|
| 44 |
+
scipy
|
| 45 |
+
sklearn
|
| 46 |
+
|
| 47 |
+
[tests]
|
| 48 |
+
asynctest
|
| 49 |
+
codecov
|
| 50 |
+
flake8
|
| 51 |
+
interrogate
|
| 52 |
+
isort==4.3.21
|
| 53 |
+
kwarray
|
| 54 |
+
onnx==1.7.0
|
| 55 |
+
onnxruntime==1.5.1
|
| 56 |
+
pytest
|
| 57 |
+
ubelt
|
| 58 |
+
xdoctest>=0.10.0
|
| 59 |
+
yapf
|
mmdet.egg-info/top_level.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
mmcv_custom
|
| 2 |
+
mmdet
|
work_dirs/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco/epoch_200.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3b7c9db4065f55509b120f5867c1b038597e7f2d6cefbe1ccfe997ffe057caa4
|
| 3 |
+
size 538113079
|
work_dirs/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco/latest.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3b7c9db4065f55509b120f5867c1b038597e7f2d6cefbe1ccfe997ffe057caa4
|
| 3 |
+
size 538113079
|
work_dirs/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py
ADDED
|
@@ -0,0 +1,300 @@
|
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|
| 1 |
+
model = dict(
|
| 2 |
+
type='MaskRCNN',
|
| 3 |
+
pretrained=None,
|
| 4 |
+
backbone=dict(
|
| 5 |
+
type='SwinTransformer',
|
| 6 |
+
embed_dim=96,
|
| 7 |
+
depths=[2, 2, 6, 2],
|
| 8 |
+
num_heads=[3, 6, 12, 24],
|
| 9 |
+
window_size=7,
|
| 10 |
+
mlp_ratio=4.0,
|
| 11 |
+
qkv_bias=True,
|
| 12 |
+
qk_scale=None,
|
| 13 |
+
drop_rate=0.0,
|
| 14 |
+
attn_drop_rate=0.0,
|
| 15 |
+
drop_path_rate=0.2,
|
| 16 |
+
ape=False,
|
| 17 |
+
patch_norm=True,
|
| 18 |
+
out_indices=(0, 1, 2, 3),
|
| 19 |
+
use_checkpoint=False),
|
| 20 |
+
neck=dict(
|
| 21 |
+
type='FPN',
|
| 22 |
+
in_channels=[96, 192, 384, 768],
|
| 23 |
+
out_channels=256,
|
| 24 |
+
num_outs=5),
|
| 25 |
+
rpn_head=dict(
|
| 26 |
+
type='RPNHead',
|
| 27 |
+
in_channels=256,
|
| 28 |
+
feat_channels=256,
|
| 29 |
+
anchor_generator=dict(
|
| 30 |
+
type='AnchorGenerator',
|
| 31 |
+
scales=[8],
|
| 32 |
+
ratios=[0.5, 1.0, 2.0],
|
| 33 |
+
strides=[4, 8, 16, 32, 64]),
|
| 34 |
+
bbox_coder=dict(
|
| 35 |
+
type='DeltaXYWHBBoxCoder',
|
| 36 |
+
target_means=[0.0, 0.0, 0.0, 0.0],
|
| 37 |
+
target_stds=[1.0, 1.0, 1.0, 1.0]),
|
| 38 |
+
loss_cls=dict(
|
| 39 |
+
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
|
| 40 |
+
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
|
| 41 |
+
roi_head=dict(
|
| 42 |
+
type='StandardRoIHead',
|
| 43 |
+
bbox_roi_extractor=dict(
|
| 44 |
+
type='SingleRoIExtractor',
|
| 45 |
+
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
|
| 46 |
+
out_channels=256,
|
| 47 |
+
featmap_strides=[4, 8, 16, 32]),
|
| 48 |
+
bbox_head=dict(
|
| 49 |
+
type='Shared2FCBBoxHead',
|
| 50 |
+
in_channels=256,
|
| 51 |
+
fc_out_channels=1024,
|
| 52 |
+
roi_feat_size=7,
|
| 53 |
+
num_classes=12,
|
| 54 |
+
bbox_coder=dict(
|
| 55 |
+
type='DeltaXYWHBBoxCoder',
|
| 56 |
+
target_means=[0.0, 0.0, 0.0, 0.0],
|
| 57 |
+
target_stds=[0.1, 0.1, 0.2, 0.2]),
|
| 58 |
+
reg_class_agnostic=False,
|
| 59 |
+
loss_cls=dict(
|
| 60 |
+
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
|
| 61 |
+
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
|
| 62 |
+
train_cfg=dict(
|
| 63 |
+
rpn=dict(
|
| 64 |
+
assigner=dict(
|
| 65 |
+
type='MaxIoUAssigner',
|
| 66 |
+
pos_iou_thr=0.7,
|
| 67 |
+
neg_iou_thr=0.3,
|
| 68 |
+
min_pos_iou=0.3,
|
| 69 |
+
match_low_quality=True,
|
| 70 |
+
ignore_iof_thr=-1),
|
| 71 |
+
sampler=dict(
|
| 72 |
+
type='RandomSampler',
|
| 73 |
+
num=256,
|
| 74 |
+
pos_fraction=0.5,
|
| 75 |
+
neg_pos_ub=-1,
|
| 76 |
+
add_gt_as_proposals=False),
|
| 77 |
+
allowed_border=-1,
|
| 78 |
+
pos_weight=-1,
|
| 79 |
+
debug=False),
|
| 80 |
+
rpn_proposal=dict(
|
| 81 |
+
nms_pre=2000,
|
| 82 |
+
max_per_img=1000,
|
| 83 |
+
nms=dict(type='nms', iou_threshold=0.7),
|
| 84 |
+
min_bbox_size=0),
|
| 85 |
+
rcnn=dict(
|
| 86 |
+
assigner=dict(
|
| 87 |
+
type='MaxIoUAssigner',
|
| 88 |
+
pos_iou_thr=0.5,
|
| 89 |
+
neg_iou_thr=0.5,
|
| 90 |
+
min_pos_iou=0.5,
|
| 91 |
+
match_low_quality=True,
|
| 92 |
+
ignore_iof_thr=-1),
|
| 93 |
+
sampler=dict(
|
| 94 |
+
type='RandomSampler',
|
| 95 |
+
num=512,
|
| 96 |
+
pos_fraction=0.25,
|
| 97 |
+
neg_pos_ub=-1,
|
| 98 |
+
add_gt_as_proposals=True),
|
| 99 |
+
mask_size=28,
|
| 100 |
+
pos_weight=-1,
|
| 101 |
+
debug=False)),
|
| 102 |
+
test_cfg=dict(
|
| 103 |
+
rpn=dict(
|
| 104 |
+
nms_pre=1000,
|
| 105 |
+
max_per_img=1000,
|
| 106 |
+
nms=dict(type='nms', iou_threshold=0.7),
|
| 107 |
+
min_bbox_size=0),
|
| 108 |
+
rcnn=dict(
|
| 109 |
+
score_thr=0.05,
|
| 110 |
+
nms=dict(type='nms', iou_threshold=0.5),
|
| 111 |
+
max_per_img=100,
|
| 112 |
+
mask_thr_binary=0.5)))
|
| 113 |
+
dataset_type = 'CocoDataset'
|
| 114 |
+
data_root = 'data/coco/'
|
| 115 |
+
img_norm_cfg = dict(
|
| 116 |
+
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
| 117 |
+
train_pipeline = [
|
| 118 |
+
dict(type='LoadImageFromFile'),
|
| 119 |
+
dict(type='LoadAnnotations', with_bbox=True, with_mask=False),
|
| 120 |
+
dict(type='RandomFlip', flip_ratio=0.5),
|
| 121 |
+
dict(
|
| 122 |
+
type='AutoAugment',
|
| 123 |
+
policies=[[{
|
| 124 |
+
'type': 'Resize',
|
| 125 |
+
'img_scale': [(224, 224)],
|
| 126 |
+
'multiscale_mode': 'value',
|
| 127 |
+
'keep_ratio': True
|
| 128 |
+
}],
|
| 129 |
+
[{
|
| 130 |
+
'type': 'Resize',
|
| 131 |
+
'img_scale': [(224, 224)],
|
| 132 |
+
'multiscale_mode': 'value',
|
| 133 |
+
'keep_ratio': True
|
| 134 |
+
}, {
|
| 135 |
+
'type': 'RandomCrop',
|
| 136 |
+
'crop_type': 'absolute_range',
|
| 137 |
+
'crop_size': (384, 600),
|
| 138 |
+
'allow_negative_crop': True
|
| 139 |
+
}, {
|
| 140 |
+
'type': 'Resize',
|
| 141 |
+
'img_scale': [(224, 224)],
|
| 142 |
+
'multiscale_mode': 'value',
|
| 143 |
+
'override': True,
|
| 144 |
+
'keep_ratio': True
|
| 145 |
+
}]]),
|
| 146 |
+
dict(
|
| 147 |
+
type='Normalize',
|
| 148 |
+
mean=[123.675, 116.28, 103.53],
|
| 149 |
+
std=[58.395, 57.12, 57.375],
|
| 150 |
+
to_rgb=True),
|
| 151 |
+
dict(type='Pad', size_divisor=32),
|
| 152 |
+
dict(type='DefaultFormatBundle'),
|
| 153 |
+
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
|
| 154 |
+
]
|
| 155 |
+
test_pipeline = [
|
| 156 |
+
dict(type='LoadImageFromFile'),
|
| 157 |
+
dict(
|
| 158 |
+
type='MultiScaleFlipAug',
|
| 159 |
+
img_scale=[(224, 224)],
|
| 160 |
+
flip=False,
|
| 161 |
+
transforms=[
|
| 162 |
+
dict(type='Resize', keep_ratio=True),
|
| 163 |
+
dict(type='RandomFlip'),
|
| 164 |
+
dict(
|
| 165 |
+
type='Normalize',
|
| 166 |
+
mean=[123.675, 116.28, 103.53],
|
| 167 |
+
std=[58.395, 57.12, 57.375],
|
| 168 |
+
to_rgb=True),
|
| 169 |
+
dict(type='Pad', size_divisor=32),
|
| 170 |
+
dict(type='ImageToTensor', keys=['img']),
|
| 171 |
+
dict(type='Collect', keys=['img'])
|
| 172 |
+
])
|
| 173 |
+
]
|
| 174 |
+
data = dict(
|
| 175 |
+
samples_per_gpu=1,
|
| 176 |
+
workers_per_gpu=8,
|
| 177 |
+
train=dict(
|
| 178 |
+
type='CocoDataset',
|
| 179 |
+
ann_file='data/coco/annotations/train.json',
|
| 180 |
+
img_prefix='data/coco/train/',
|
| 181 |
+
pipeline=[
|
| 182 |
+
dict(type='LoadImageFromFile'),
|
| 183 |
+
dict(type='LoadAnnotations', with_bbox=True, with_mask=False),
|
| 184 |
+
dict(type='RandomFlip', flip_ratio=0.5),
|
| 185 |
+
dict(
|
| 186 |
+
type='AutoAugment',
|
| 187 |
+
policies=[[{
|
| 188 |
+
'type': 'Resize',
|
| 189 |
+
'img_scale': [(224, 224)],
|
| 190 |
+
'multiscale_mode': 'value',
|
| 191 |
+
'keep_ratio': True
|
| 192 |
+
}],
|
| 193 |
+
[{
|
| 194 |
+
'type': 'Resize',
|
| 195 |
+
'img_scale': [(224, 224)],
|
| 196 |
+
'multiscale_mode': 'value',
|
| 197 |
+
'keep_ratio': True
|
| 198 |
+
}, {
|
| 199 |
+
'type': 'RandomCrop',
|
| 200 |
+
'crop_type': 'absolute_range',
|
| 201 |
+
'crop_size': (384, 600),
|
| 202 |
+
'allow_negative_crop': True
|
| 203 |
+
}, {
|
| 204 |
+
'type': 'Resize',
|
| 205 |
+
'img_scale': [(224, 224)],
|
| 206 |
+
'multiscale_mode': 'value',
|
| 207 |
+
'override': True,
|
| 208 |
+
'keep_ratio': True
|
| 209 |
+
}]]),
|
| 210 |
+
dict(
|
| 211 |
+
type='Normalize',
|
| 212 |
+
mean=[123.675, 116.28, 103.53],
|
| 213 |
+
std=[58.395, 57.12, 57.375],
|
| 214 |
+
to_rgb=True),
|
| 215 |
+
dict(type='Pad', size_divisor=32),
|
| 216 |
+
dict(type='DefaultFormatBundle'),
|
| 217 |
+
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
|
| 218 |
+
]),
|
| 219 |
+
val=dict(
|
| 220 |
+
type='CocoDataset',
|
| 221 |
+
ann_file='data/coco/annotations/val.json',
|
| 222 |
+
img_prefix='data/coco/val/',
|
| 223 |
+
pipeline=[
|
| 224 |
+
dict(type='LoadImageFromFile'),
|
| 225 |
+
dict(
|
| 226 |
+
type='MultiScaleFlipAug',
|
| 227 |
+
img_scale=[(224, 224)],
|
| 228 |
+
flip=False,
|
| 229 |
+
transforms=[
|
| 230 |
+
dict(type='Resize', keep_ratio=True),
|
| 231 |
+
dict(type='RandomFlip'),
|
| 232 |
+
dict(
|
| 233 |
+
type='Normalize',
|
| 234 |
+
mean=[123.675, 116.28, 103.53],
|
| 235 |
+
std=[58.395, 57.12, 57.375],
|
| 236 |
+
to_rgb=True),
|
| 237 |
+
dict(type='Pad', size_divisor=32),
|
| 238 |
+
dict(type='ImageToTensor', keys=['img']),
|
| 239 |
+
dict(type='Collect', keys=['img'])
|
| 240 |
+
])
|
| 241 |
+
]),
|
| 242 |
+
test=dict(
|
| 243 |
+
type='CocoDataset',
|
| 244 |
+
ann_file='data/coco/annotations/val.json',
|
| 245 |
+
img_prefix='data/coco/val/',
|
| 246 |
+
pipeline=[
|
| 247 |
+
dict(type='LoadImageFromFile'),
|
| 248 |
+
dict(
|
| 249 |
+
type='MultiScaleFlipAug',
|
| 250 |
+
img_scale=[(224, 224)],
|
| 251 |
+
flip=False,
|
| 252 |
+
transforms=[
|
| 253 |
+
dict(type='Resize', keep_ratio=True),
|
| 254 |
+
dict(type='RandomFlip'),
|
| 255 |
+
dict(
|
| 256 |
+
type='Normalize',
|
| 257 |
+
mean=[123.675, 116.28, 103.53],
|
| 258 |
+
std=[58.395, 57.12, 57.375],
|
| 259 |
+
to_rgb=True),
|
| 260 |
+
dict(type='Pad', size_divisor=32),
|
| 261 |
+
dict(type='ImageToTensor', keys=['img']),
|
| 262 |
+
dict(type='Collect', keys=['img'])
|
| 263 |
+
])
|
| 264 |
+
]))
|
| 265 |
+
evaluation = dict(interval=1, metric='bbox')
|
| 266 |
+
optimizer = dict(
|
| 267 |
+
type='AdamW',
|
| 268 |
+
lr=0.0001,
|
| 269 |
+
betas=(0.9, 0.999),
|
| 270 |
+
weight_decay=0.05,
|
| 271 |
+
paramwise_cfg=dict(
|
| 272 |
+
custom_keys=dict(
|
| 273 |
+
absolute_pos_embed=dict(decay_mult=0.0),
|
| 274 |
+
relative_position_bias_table=dict(decay_mult=0.0),
|
| 275 |
+
norm=dict(decay_mult=0.0))))
|
| 276 |
+
optimizer_config = dict(
|
| 277 |
+
grad_clip=None,
|
| 278 |
+
type='DistOptimizerHook',
|
| 279 |
+
update_interval=1,
|
| 280 |
+
coalesce=True,
|
| 281 |
+
bucket_size_mb=-1,
|
| 282 |
+
use_fp16=True)
|
| 283 |
+
lr_config = dict(
|
| 284 |
+
policy='step',
|
| 285 |
+
warmup='linear',
|
| 286 |
+
warmup_iters=500,
|
| 287 |
+
warmup_ratio=0.001,
|
| 288 |
+
step=[27, 33])
|
| 289 |
+
runner = dict(type='EpochBasedRunnerAmp', max_epochs=200)
|
| 290 |
+
checkpoint_config = dict(interval=25)
|
| 291 |
+
log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')])
|
| 292 |
+
custom_hooks = [dict(type='NumClassCheckHook')]
|
| 293 |
+
dist_params = dict(backend='nccl')
|
| 294 |
+
log_level = 'INFO'
|
| 295 |
+
load_from = None
|
| 296 |
+
resume_from = 'checkpoints/epoch_75.pth'
|
| 297 |
+
workflow = [('train', 1)]
|
| 298 |
+
fp16 = None
|
| 299 |
+
work_dir = './work_dirs\mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco'
|
| 300 |
+
gpu_ids = range(0, 1)
|