OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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62 lines
2.6 KiB
62 lines
2.6 KiB
# Timm Example |
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> [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models) |
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## Abstract |
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Py**T**orch **Im**age **M**odels (`timm`) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results. |
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## Results and Models |
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### RetinaNet |
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| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | |
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| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------: | :------: | |
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| R-50 | pytorch | 1x | | | | [config](./retinanet_timm_tv_resnet50_fpn_1x_coco.py) | | |
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| EfficientNet-B1 | - | 1x | | | | [config](./retinanet_timm_efficientnet_b1_fpn_1x_coco.py) | | |
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## Usage |
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### Install additional requirements |
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MMDetection supports timm backbones via `TIMMBackbone`, a wrapper class in MMClassification. |
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Thus, you need to install `mmcls` in addition to timm. |
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If you have already installed requirements for mmdet, run |
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```shell |
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pip install 'dataclasses; python_version<"3.7"' |
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pip install timm |
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pip install 'mmcls>=0.20.0' |
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``` |
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See [this document](https://mmclassification.readthedocs.io/en/latest/install.html) for the details of MMClassification installation. |
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### Edit config |
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- See example configs for basic usage. |
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- See the documents of [timm feature extraction](https://rwightman.github.io/pytorch-image-models/feature_extraction/#multi-scale-feature-maps-feature-pyramid) and [TIMMBackbone](https://mmclassification.readthedocs.io/en/latest/api.html#mmcls.models.backbones.TIMMBackbone) for details. |
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- Which feature map is output depends on the backbone. |
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Please check `backbone out_channels` and `backbone out_strides` in your log, and modify `model.neck.in_channels` and `model.backbone.out_indices` if necessary. |
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- If you use Vision Transformer models that do not support `features_only=True`, add `custom_hooks = []` to your config to disable `NumClassCheckHook`. |
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## Citation |
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```latex |
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@misc{rw2019timm, |
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author = {Ross Wightman}, |
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title = {PyTorch Image Models}, |
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year = {2019}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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doi = {10.5281/zenodo.4414861}, |
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howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} |
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} |
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```
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