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[Reproduce] Reproduce RepVGG Training Accuracy. (#1264)
* repr repvgg * add VisionRRC * uodate repvgg configs * add BCD seriers cfgs * add cv backend config * add vision configs * add L2se configs * add ra configs * add num-works configs * add num-works configs * configs * update README * rm extra config * reset un-needed changes * update * reset pbn * update readme * update code * update code * refine doc
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# RepVGG
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> [Repvgg: Making vgg-style convnets great again](https://arxiv.org/abs/2101.03697)
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> [RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697)
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<!-- [ALGORITHM] -->
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## Abstract
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## Introduction
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We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet.
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RepVGG is a VGG-style convolutional architecture. It has the following advantages:
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1. The model has a VGG-like plain (a.k.a. feed-forward) topology 1 without any branches. I.e., every layer takes the output of its only preceding layer as input and feeds the output into its only following layer.
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2. The model’s body uses only 3 × 3 conv and ReLU.
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3. The concrete architecture (including the specific depth and layer widths) is instantiated with no automatic search, manual refinement, compound scaling, nor other heavy designs.
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<div align=center>
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<img src="https://user-images.githubusercontent.com/26739999/142573223-f7f14d32-ea08-43a1-81ad-5a6a83ee0122.png" width="60%"/>
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</div>
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## Results and models
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## Abstract
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### ImageNet-1k
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<details>
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| Model | Epochs | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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| :-----------: | :----: | :-------------------------------: | :-----------------------------: | :-------: | :-------: | :----------------------------------------------: | :-------------------------------------------------: |
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| RepVGG-A0\* | 120 | 9.11（train) \| 8.31 (deploy) | 1.52 (train) \| 1.36 (deploy) | 72.41 | 90.50 | [config (train)](./repvgg-A0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](./deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth) |
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| RepVGG-A1\* | 120 | 14.09 (train) \| 12.79 (deploy) | 2.64 (train) \| 2.37 (deploy) | 74.47 | 91.85 | [config (train)](./repvgg-A1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](./deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth) |
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| RepVGG-A2\* | 120 | 28.21 (train) \| 25.5 (deploy) | 5.7 (train) \| 5.12 (deploy) | 76.48 | 93.01 | [config (train)](./repvgg-A2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](./deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) |
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| RepVGG-B0\* | 120 | 15.82 (train) \| 14.34 (deploy) | 3.42 (train) \| 3.06 (deploy) | 75.14 | 92.42 | [config (train)](./repvgg-B0_4xb64-coslr-120e_in1k.py) \|[config (deploy)](./deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth) |
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| RepVGG-B1\* | 120 | 57.42 (train) \| 51.83 (deploy) | 13.16 (train) \| 11.82 (deploy) | 78.37 | 94.11 | [config (train)](./repvgg-B1_4xb64-coslr-120e_in1k.py) \|[config (deploy)](./deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) |
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| RepVGG-B1g2\* | 120 | 45.78 (train) \| 41.36 (deploy) | 9.82 (train) \| 8.82 (deploy) | 77.79 | 93.88 | [config (train)](./repvgg-B1g2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](./deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth) |
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| RepVGG-B1g4\* | 120 | 39.97 (train) \| 36.13 (deploy) | 8.15 (train) \| 7.32 (deploy) | 77.58 | 93.84 | [config (train)](./repvgg-B1g4_4xb64-coslr-120e_in1k.py) \|[config (deploy)](./deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth) |
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| RepVGG-B2\* | 120 | 89.02 (train) \| 80.32 (deploy) | 20.46 (train) \| 18.39 (deploy) | 78.78 | 94.42 | [config (train)](./repvgg-B2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](./deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth) |
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| RepVGG-B2g4\* | 200 | 61.76 (train) \| 55.78 (deploy) | 12.63 (train) \| 11.34 (deploy) | 79.38 | 94.68 | [config (train)](./repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](./deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth) |
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| RepVGG-B3\* | 200 | 123.09 (train) \| 110.96 (deploy) | 29.17 (train) \| 26.22 (deploy) | 80.52 | 95.26 | [config (train)](./repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](./deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth) |
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| RepVGG-B3g4\* | 200 | 83.83 (train) \| 75.63 (deploy) | 17.9 (train) \| 16.08 (deploy) | 80.22 | 95.10 | [config (train)](./repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](./deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth) |
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| RepVGG-D2se\* | 200 | 133.33 (train) \| 120.39 (deploy) | 36.56 (train) \| 32.85 (deploy) | 81.81 | 95.94 | [config (train)](./repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](./deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) |
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<summary>Show the paper's abstract</summary>
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*Models with * are converted from the [official repo](https://github.com/DingXiaoH/RepVGG). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
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<br>
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We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet.
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</br>
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</details>
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## How to use
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The checkpoints provided are all `training-time` models. Use the reparameterize tool to switch them to more efficient `inference-time` architecture, which not only has fewer parameters but also less calculations.
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The checkpoints provided are all `training-time` models. Use the reparameterize tool or `switch_to_deploy` interface to switch them to more efficient `inference-time` architecture, which not only has fewer parameters but also less calculations.
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### Use tool
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<!-- [TABS-BEGIN] -->
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**Predict image**
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Use `classifier.backbone.switch_to_deploy()` interface to switch the RepVGG models into inference mode.
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```python
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>>> import torch
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>>> from mmcls.apis import init_model, inference_model
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>>>
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>>> model = init_model('configs/repvgg/repvgg-A0_8xb32_in1k.py', 'https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth')
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>>> results = inference_model(model, 'demo/demo.JPEG')
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>>> print( (results['pred_class'], results['pred_score']) )
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('sea snake' 0.8338906168937683)
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>>>
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>>> # switch to deploy mode
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>>> model.backbone.switch_to_deploy()
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>>> results = inference_model(model, 'demo/demo.JPEG')
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>>> print( (results['pred_class'], results['pred_score']) )
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('sea snake', 0.7883061170578003)
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```
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**Use the model**
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```python
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>>> import torch
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>>> from mmcls.apis import get_model
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>>>
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>>> model = get_model("repvgg-a0_8xb32_in1k", pretrained=True)
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>>> model.eval()
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>>> inputs = torch.rand(1, 3, 224, 224).to(model.data_preprocessor.device)
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>>> # To get classification scores.
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>>> out = model(inputs)
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>>> print(out.shape)
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torch.Size([1, 1000])
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>>> # To extract features.
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>>> outs = model.extract_feat(inputs)
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>>> print(outs[0].shape)
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torch.Size([1, 1280])
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>>>
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>>> # switch to deploy mode
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>>> model.backbone.switch_to_deploy()
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>>> out_deploy = model(inputs)
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>>> print(out.shape)
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torch.Size([1, 1000])
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>>> assert torch.allclose(out, out_deploy, rtol=1e-4, atol=1e-5) # pass without error
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```
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**Train/Test Command**
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Place the ImageNet dataset to the `data/imagenet/` directory, or prepare datasets according to the [docs](https://mmclassification.readthedocs.io/en/1.x/user_guides/dataset_prepare.html#prepare-dataset).
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Train:
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```shell
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python tools/train.py configs/repvgg/repvgg-a0_8xb32_in1k.py
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```
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Download Checkpoint:
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```shell
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wget https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth
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```
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Test use unfused model:
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```shell
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python tools/test.py configs/repvgg/repvgg-a0_8xb32_in1k.py repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth
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```
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Reparameterize checkpoint:
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```shell
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python ./tools/convert_models/reparameterize_model.py configs/repvgg/repvgg-a0_8xb32_in1k.py repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth repvgg_A0_deploy.pth
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```
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Test use fused model:
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```shell
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python tools/test.py configs/repvgg/repvgg-A0_8xb32_in1k.py repvgg_A0_deploy.pth --cfg-options model.backbone.deploy=True
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```
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or
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```shell
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python tools/test.py configs/repvgg/repvgg-A0_deploy_in1k.py repvgg_A0_deploy.pth
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```
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<!-- [TABS-END] -->
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For more configurable parameters, please refer to the [API](https://mmclassification.readthedocs.io/en/1.x/api/generated/mmcls.models.backbones.RepVGG.html#mmcls.models.backbones.RepVGG).
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<details>
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<summary><b>How to use the reparameterisation tool</b>(click to show)</summary>
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<br>
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Use provided tool to reparameterize the given model and save the checkpoint:
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@ -45,52 +136,68 @@ Use provided tool to reparameterize the given model and save the checkpoint:
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python tools/convert_models/reparameterize_model.py ${CFG_PATH} ${SRC_CKPT_PATH} ${TARGET_CKPT_PATH}
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```
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`${CFG_PATH}` is the config file, `${SRC_CKPT_PATH}` is the source chenpoint file, `${TARGET_CKPT_PATH}` is the target deploy weight file path.
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`${CFG_PATH}` is the config file path, `${SRC_CKPT_PATH}` is the source chenpoint file path, `${TARGET_CKPT_PATH}` is the target deploy weight file path.
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To use reparameterized weights, the config file must switch to the deploy config files.
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For example:
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```bash
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python tools/test.py ${Deploy_CFG} ${Deploy_Checkpoint} --metrics accuracy
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```shell
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# download the weight
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wget https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth
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# reparameterize unfused weight to fused weight
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python ./tools/convert_models/reparameterize_model.py configs/repvgg/repvgg-a0_8xb32_in1k.py repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth repvgg-A0_deploy.pth
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```
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### In the code
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To use reparameterized weights, the config file must switch to **the deploy config files** as [the deploy_A0 example](./repvgg-A0_deploy_in1k.py) or add `--cfg-options model.backbone.deploy=True` in command.
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Use `backbone.switch_to_deploy()` or `classificer.backbone.switch_to_deploy()` to switch to the deploy mode. For example:
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For example of using the reparameterized weights above:
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```python
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from mmcls.models import build_backbone
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backbone_cfg=dict(type='RepVGG',arch='A0'),
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backbone = build_backbone(backbone_cfg)
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backbone.switch_to_deploy()
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```shell
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python ./tools/test.py ./configs/repvgg/repvgg-A0_deploy_in1k.py repvgg-A0_deploy.pth
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```
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or
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You can get other deploy configs by modifying the [A0_deploy example](./repvgg-A0_deploy_in1k.py):
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```python
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from mmcls.models import build_classifier
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```text
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# in repvgg-A0_deploy_in1k.py
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_base_ = '../repvgg-A0_8xb32_in1k.py' # basic A0 config
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cfg = dict(
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type='ImageClassifier',
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backbone=dict(
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type='RepVGG',
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arch='A0'),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='LinearClsHead',
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num_classes=1000,
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in_channels=1280,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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topk=(1, 5),
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))
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classifier = build_classifier(cfg)
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classifier.backbone.switch_to_deploy()
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model = dict(backbone=dict(deploy=True)) # switch model into deploy mode
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```
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or add `--cfg-options model.backbone.deploy=True` in command as following：
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```shell
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python tools/test.py configs/repvgg/repvgg-A0_8xb32_in1k.py repvgg_A0_deploy.pth --cfg-options model.backbone.deploy=True
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```
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</br>
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</details>
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## Results and models
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### ImageNet-1k
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| Model | Pretrain | <p> Params(M) <br>（train\|deploy) </p> | <p> Flops(G) <br>（train\|deploy) </p> | Top-1 (%) | Top-5 (%) | Config | Download |
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| :-------------------------: | :----------: | :-------------------------------------: | :--------------------------------------: | :-------: | :-------: | :-----------------------------: | :-------------------------------: |
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| repvgg-A0_8xb32_in1k | From scratch | 9.11 \| 8.31 | 1.53 \| 1.36 | 72.37 | 90.56 | [config](./repvgg-A0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.log) |
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| repvgg-A1_8xb32_in1k | From scratch | 14.09 \| 12.79 | 2.65 \| 2.37 | 74.47 | 91.85 | [config](./repvgg-A1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_8xb32_in1k_20221213-f81bf3df.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_8xb32_in1k_20221213-f81bf3df.log) |
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| repvgg-A2_8xb32_in1k | From scratch | 28.21 \| 25.5 | 5.72 \| 5.12 | 76.49 | 93.09 | [config](./repvgg-A2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_8xb32_in1k_20221213-a8767caf.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_8xb32_in1k_20221213-a8767caf.log) |
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| repvgg-B0_8xb32_in1k | From scratch | 15.82 \| 14.34 | 3.43 \| 3.06 | 75.27 | 92.21 | [config](./repvgg-B0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_8xb32_in1k_20221213-5091ecc7.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_8xb32_in1k_20221213-5091ecc7.log) |
|
||||
| repvgg-B1_8xb32_in1k | From scratch | 57.42 \| 51.83 | 13.20 \| 11.81 | 78.19 | 94.04 | [config](./repvgg-B1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_8xb32_in1k_20221213-d17c45e7.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_8xb32_in1k_20221213-d17c45e7.log) |
|
||||
| repvgg-B1g2_8xb32_in1k | From scratch | 45.78 \| 41.36 | 9.86 \| 8.80 | 77.87 | 93.99 | [config](./repvgg-B1g2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_8xb32_in1k_20221213-ae6428fd.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_8xb32_in1k_20221213-ae6428fd.log) |
|
||||
| repvgg-B1g4_8xb32_in1k | From scratch | 39.97 \| 36.13 | 8.19 \| 7.30 | 77.81 | 93.77 | [config](./repvgg-B1g4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_8xb32_in1k_20221213-a7a4aaea.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_8xb32_in1k_20221213-a7a4aaea.log) |
|
||||
| repvgg-B2_8xb32_in1k | From scratch | 89.02 \| 80.32 | 20.5 \| 18.4 | 78.58 | 94.23 | [config](./repvgg-B2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_8xb32_in1k_20221213-d8b420ef.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_8xb32_in1k_20221213-d8b420ef.log) |
|
||||
| repvgg-B2g4_8xb32_in1k | From scratch | 61.76 \| 55.78 | 12.7 \| 11.3 | 79.44 | 94.72 | [config](./repvgg-B2g4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_8xb32_in1k_20221213-0c1990eb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_8xb32_in1k_20221213-0c1990eb.log) |
|
||||
| repvgg-B3_8xb32_in1k | From scratch | 123.09 \| 110.96 | 29.2 \| 26.2 | 80.58 | 95.33 | [config](./repvgg-B3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_8xb32_in1k_20221213-927a329a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_8xb32_in1k_20221213-927a329a.log) |
|
||||
| repvgg-B3g4_8xb32_in1k | From scratch | 83.83 \| 75.63 | 18.0 \| 16.1 | 80.26 | 95.15 | [config](./repvgg-B3g4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_8xb32_in1k_20221213-e01cb280.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_8xb32_in1k_20221213-e01cb280.log) |
|
||||
| repvgg-D2se_3rdparty_in1k\* | From scratch | 133.33 \| 120.39 | 36.6 \| 32.8 | 81.81 | 95.94 | [config](./repvgg-D2se_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) |
|
||||
|
||||
*Models with * are converted from the [official repo](https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L250). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
|
||||
|
||||
## Citation
|
||||
|
||||
```
|
||||
```bibtex
|
||||
@inproceedings{ding2021repvgg,
|
||||
title={Repvgg: Making vgg-style convnets great again},
|
||||
author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian},
|
||||
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-A0_4xb64-coslr-120e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-A1_4xb64-coslr-120e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-A2_4xb64-coslr-120e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-B0_4xb64-coslr-120e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-B1_4xb64-coslr-120e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-B1g2_4xb64-coslr-120e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-B1g4_4xb64-coslr-120e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-B2_4xb64-coslr-120e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = '../repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -14,57 +14,48 @@ Collections:
|
||||
Version: v0.16.0
|
||||
|
||||
Models:
|
||||
- Name: repvgg-A0_3rdparty_4xb64-coslr-120e_in1k
|
||||
- Name: repvgg-A0_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py
|
||||
Config: configs/repvgg/repvgg-A0_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 1520000000
|
||||
Parameters: 9110000
|
||||
FLOPs: 1360233728
|
||||
Parameters: 8309384
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 72.41
|
||||
Top 5 Accuracy: 90.50
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L196
|
||||
- Name: repvgg-A1_3rdparty_4xb64-coslr-120e_in1k
|
||||
Top 1 Accuracy: 72.37
|
||||
Top 5 Accuracy: 90.56
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth
|
||||
- Name: repvgg-A1_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py
|
||||
Config: configs/repvgg/repvgg-A1_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 2640000000
|
||||
Parameters: 14090000
|
||||
FLOPs: 2362750208
|
||||
Parameters: 12789864
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 74.47
|
||||
Top 5 Accuracy: 91.85
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L200
|
||||
- Name: repvgg-A2_3rdparty_4xb64-coslr-120e_in1k
|
||||
Top 1 Accuracy: 74.23
|
||||
Top 5 Accuracy: 91.80
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_8xb32_in1k_20221213-f81bf3df.pth
|
||||
- Name: repvgg-A2_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py
|
||||
Config: configs/repvgg/repvgg-A2_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 28210000000
|
||||
Parameters: 5700000
|
||||
FLOPs: 5115612544
|
||||
Parameters: 25499944
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 76.48
|
||||
Top 5 Accuracy: 93.01
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L204
|
||||
- Name: repvgg-B0_3rdparty_4xb64-coslr-120e_in1k
|
||||
Top 1 Accuracy: 76.49
|
||||
Top 5 Accuracy: 93.09
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_8xb32_in1k_20221213-a8767caf.pth
|
||||
- Name: repvgg-B0_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py
|
||||
Config: configs/repvgg/repvgg-B0_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 15820000000
|
||||
Parameters: 3420000
|
||||
@ -72,130 +63,106 @@ Models:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 75.14
|
||||
Top 5 Accuracy: 92.42
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L208
|
||||
- Name: repvgg-B1_3rdparty_4xb64-coslr-120e_in1k
|
||||
Top 1 Accuracy: 75.27
|
||||
Top 5 Accuracy: 92.21
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_8xb32_in1k_20221213-5091ecc7.pth
|
||||
- Name: repvgg-B1_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py
|
||||
Config: configs/repvgg/repvgg-B1_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 57420000000
|
||||
Parameters: 13160000
|
||||
FLOPs: 11813537792
|
||||
Parameters: 51829480
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 78.37
|
||||
Top 5 Accuracy: 94.11
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L212
|
||||
- Name: repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k
|
||||
Top 1 Accuracy: 78.19
|
||||
Top 5 Accuracy: 94.04
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_8xb32_in1k_20221213-d17c45e7.pth
|
||||
- Name: repvgg-B1g2_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py
|
||||
Config: configs/repvgg/repvgg-B1g2_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 45780000000
|
||||
Parameters: 9820000
|
||||
FLOPs: 8807794688
|
||||
Parameters: 41360104
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 77.79
|
||||
Top 5 Accuracy: 93.88
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L216
|
||||
- Name: repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k
|
||||
Top 1 Accuracy: 77.87
|
||||
Top 5 Accuracy: 93.99
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_8xb32_in1k_20221213-ae6428fd.pth
|
||||
- Name: repvgg-B1g4_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py
|
||||
Config: configs/repvgg/repvgg-B1g4_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 39970000000
|
||||
Parameters: 8150000
|
||||
FLOPs: 7304923136
|
||||
Parameters: 36125416
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 77.58
|
||||
Top 5 Accuracy: 93.84
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L220
|
||||
- Name: repvgg-B2_3rdparty_4xb64-coslr-120e_in1k
|
||||
Top 1 Accuracy: 77.81
|
||||
Top 5 Accuracy: 93.77
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_8xb32_in1k_20221213-a7a4aaea.pth
|
||||
- Name: repvgg-B2_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py
|
||||
Config: configs/repvgg/repvgg-B2_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 89020000000
|
||||
Parameters: 20420000
|
||||
FLOPs: 18374175232
|
||||
Parameters: 80315112
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 78.78
|
||||
Top 5 Accuracy: 94.42
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L225
|
||||
- Name: repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k
|
||||
Top 1 Accuracy: 78.58
|
||||
Top 5 Accuracy: 94.23
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_8xb32_in1k_20221213-d8b420ef.pth
|
||||
- Name: repvgg-B2g4_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py
|
||||
Config: configs/repvgg/repvgg-B2g4_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 61760000000
|
||||
Parameters: 12630000
|
||||
FLOPs: 11329464832
|
||||
Parameters: 55777512
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 79.38
|
||||
Top 5 Accuracy: 94.68
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L229
|
||||
- Name: repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k
|
||||
Top 1 Accuracy: 79.44
|
||||
Top 5 Accuracy: 94.72
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_8xb32_in1k_20221213-0c1990eb.pth
|
||||
- Name: repvgg-B3_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py
|
||||
Config: configs/repvgg/repvgg-B3_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 123090000000
|
||||
Parameters: 29170000
|
||||
FLOPs: 26206448128
|
||||
Parameters: 110960872
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 80.52
|
||||
Top 5 Accuracy: 95.26
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L238
|
||||
- Name: repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k
|
||||
Top 1 Accuracy: 80.58
|
||||
Top 5 Accuracy: 95.33
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_8xb32_in1k_20221213-927a329a.pth
|
||||
- Name: repvgg-B3g4_8xb32_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py
|
||||
Config: configs/repvgg/repvgg-B3g4_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 83830000000
|
||||
Parameters: 17900000
|
||||
FLOPs: 16062065152
|
||||
Parameters: 75626728
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
Metrics:
|
||||
Top 1 Accuracy: 80.22
|
||||
Top 5 Accuracy: 95.10
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth
|
||||
Converted From:
|
||||
Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq
|
||||
Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L238
|
||||
- Name: repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k
|
||||
Top 1 Accuracy: 80.26
|
||||
Top 5 Accuracy: 95.15
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_8xb32_in1k_20221213-e01cb280.pth
|
||||
- Name: repvgg-D2se_3rdparty_in1k
|
||||
In Collection: RepVGG
|
||||
Config: configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py
|
||||
Config: configs/repvgg/repvgg-D2se_8xb32_in1k.py
|
||||
Metadata:
|
||||
FLOPs: 133330000000
|
||||
Parameters: 36560000
|
||||
FLOPs: 32838581760
|
||||
Parameters: 120387572
|
||||
Results:
|
||||
- Dataset: ImageNet-1k
|
||||
Task: Image Classification
|
||||
|
||||
@ -1,12 +0,0 @@
|
||||
_base_ = [
|
||||
'../_base_/models/repvgg-A0_in1k.py',
|
||||
'../_base_/datasets/imagenet_bs64_pil_resize.py',
|
||||
'../_base_/schedules/imagenet_bs256_coslr.py',
|
||||
'../_base_/default_runtime.py'
|
||||
]
|
||||
|
||||
# schedule settings
|
||||
param_scheduler = dict(
|
||||
type='CosineAnnealingLR', T_max=120, by_epoch=True, begin=0, end=120)
|
||||
|
||||
train_cfg = dict(by_epoch=True, max_epochs=120)
|
||||
33
configs/repvgg/repvgg-A0_8xb32_in1k.py
Normal file
33
configs/repvgg/repvgg-A0_8xb32_in1k.py
Normal file
@ -0,0 +1,33 @@
|
||||
_base_ = [
|
||||
'../_base_/models/repvgg-A0_in1k.py',
|
||||
'../_base_/datasets/imagenet_bs32_pil_resize.py',
|
||||
'../_base_/schedules/imagenet_bs256_coslr.py',
|
||||
'../_base_/default_runtime.py'
|
||||
]
|
||||
|
||||
val_dataloader = dict(batch_size=256)
|
||||
test_dataloader = dict(batch_size=256)
|
||||
|
||||
# schedule settings
|
||||
optim_wrapper = dict(
|
||||
paramwise_cfg=dict(
|
||||
bias_decay_mult=0.0,
|
||||
custom_keys={
|
||||
'branch_3x3.norm': dict(decay_mult=0.0),
|
||||
'branch_1x1.norm': dict(decay_mult=0.0),
|
||||
'branch_norm.bias': dict(decay_mult=0.0),
|
||||
}))
|
||||
|
||||
# schedule settings
|
||||
param_scheduler = dict(
|
||||
type='CosineAnnealingLR',
|
||||
T_max=120,
|
||||
by_epoch=True,
|
||||
begin=0,
|
||||
end=120,
|
||||
convert_to_iter_based=True)
|
||||
|
||||
train_cfg = dict(by_epoch=True, max_epochs=120)
|
||||
|
||||
default_hooks = dict(
|
||||
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
|
||||
3
configs/repvgg/repvgg-A0_deploy_in1k.py
Normal file
3
configs/repvgg/repvgg-A0_deploy_in1k.py
Normal file
@ -0,0 +1,3 @@
|
||||
_base_ = '../repvgg-A0_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(deploy=True))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='A1'))
|
||||
3
configs/repvgg/repvgg-A1_8xb32_in1k.py
Normal file
3
configs/repvgg/repvgg-A1_8xb32_in1k.py
Normal file
@ -0,0 +1,3 @@
|
||||
_base_ = './repvgg-A0_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='A1'))
|
||||
@ -1,3 +1,3 @@
|
||||
_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py'
|
||||
_base_ = './repvgg-A0_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='A2'), head=dict(in_channels=1408))
|
||||
@ -1,3 +1,3 @@
|
||||
_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py'
|
||||
_base_ = './repvgg-A0_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='B0'), head=dict(in_channels=1280))
|
||||
@ -1,3 +1,3 @@
|
||||
_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py'
|
||||
_base_ = './repvgg-A0_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='B1'), head=dict(in_channels=2048))
|
||||
@ -1,3 +1,3 @@
|
||||
_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py'
|
||||
_base_ = './repvgg-A0_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='B1g2'), head=dict(in_channels=2048))
|
||||
@ -1,3 +1,3 @@
|
||||
_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py'
|
||||
_base_ = './repvgg-A0_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='B1g4'), head=dict(in_channels=2048))
|
||||
@ -1,3 +1,3 @@
|
||||
_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py'
|
||||
_base_ = './repvgg-A0_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='B2'), head=dict(in_channels=2560))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='B2g4'))
|
||||
3
configs/repvgg/repvgg-B2g4_8xb32_in1k.py
Normal file
3
configs/repvgg/repvgg-B2g4_8xb32_in1k.py
Normal file
@ -0,0 +1,3 @@
|
||||
_base_ = './repvgg-B3_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='B2g4'), head=dict(in_channels=2560))
|
||||
@ -1,10 +1,20 @@
|
||||
_base_ = [
|
||||
'../_base_/models/repvgg-B3_lbs-mixup_in1k.py',
|
||||
'../_base_/datasets/imagenet_bs64_pil_resize.py',
|
||||
'../_base_/schedules/imagenet_bs256_200e_coslr_warmup.py',
|
||||
'../_base_/datasets/imagenet_bs32_pil_resize.py',
|
||||
'../_base_/schedules/imagenet_bs256_coslr.py',
|
||||
'../_base_/default_runtime.py'
|
||||
]
|
||||
|
||||
# schedule settings
|
||||
optim_wrapper = dict(
|
||||
paramwise_cfg=dict(
|
||||
bias_decay_mult=0.0,
|
||||
custom_keys={
|
||||
'branch_3x3.norm': dict(decay_mult=0.0),
|
||||
'branch_1x1.norm': dict(decay_mult=0.0),
|
||||
'branch_norm.bias': dict(decay_mult=0.0),
|
||||
}))
|
||||
|
||||
data_preprocessor = dict(
|
||||
# RGB format normalization parameters
|
||||
mean=[123.675, 116.28, 103.53],
|
||||
@ -21,8 +31,12 @@ train_pipeline = [
|
||||
dict(type='RandomResizedCrop', scale=224, backend='pillow'),
|
||||
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
|
||||
dict(
|
||||
type='AutoAugment',
|
||||
policies='imagenet',
|
||||
type='RandAugment',
|
||||
policies='timm_increasing',
|
||||
num_policies=2,
|
||||
total_level=10,
|
||||
magnitude_level=7,
|
||||
magnitude_std=0.5,
|
||||
hparams=dict(pad_val=[round(x) for x in bgr_mean])),
|
||||
dict(type='PackClsInputs'),
|
||||
]
|
||||
@ -37,3 +51,17 @@ test_pipeline = [
|
||||
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
|
||||
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
|
||||
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
|
||||
|
||||
# schedule settings
|
||||
param_scheduler = dict(
|
||||
type='CosineAnnealingLR',
|
||||
T_max=200,
|
||||
by_epoch=True,
|
||||
begin=0,
|
||||
end=200,
|
||||
convert_to_iter_based=True)
|
||||
|
||||
train_cfg = dict(by_epoch=True, max_epochs=200)
|
||||
|
||||
default_hooks = dict(
|
||||
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='B3g4'))
|
||||
3
configs/repvgg/repvgg-B3g4_8xb32_in1k.py
Normal file
3
configs/repvgg/repvgg-B3g4_8xb32_in1k.py
Normal file
@ -0,0 +1,3 @@
|
||||
_base_ = './repvgg-B3_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='B3g4'))
|
||||
@ -1,3 +0,0 @@
|
||||
_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='D2se'))
|
||||
28
configs/repvgg/repvgg-D2se_8xb32_in1k.py
Normal file
28
configs/repvgg/repvgg-D2se_8xb32_in1k.py
Normal file
@ -0,0 +1,28 @@
|
||||
_base_ = './repvgg-B3_8xb32_in1k.py'
|
||||
|
||||
model = dict(backbone=dict(arch='D2se'), head=dict(in_channels=2560))
|
||||
|
||||
param_scheduler = [
|
||||
# warm up learning rate scheduler
|
||||
dict(
|
||||
type='LinearLR',
|
||||
start_factor=0.0001,
|
||||
by_epoch=True,
|
||||
begin=0,
|
||||
end=5,
|
||||
# update by iter
|
||||
convert_to_iter_based=True),
|
||||
# main learning rate scheduler
|
||||
dict(
|
||||
type='CosineAnnealingLR',
|
||||
T_max=295,
|
||||
eta_min=1.0e-6,
|
||||
by_epoch=True,
|
||||
begin=5,
|
||||
end=300)
|
||||
]
|
||||
|
||||
train_cfg = dict(by_epoch=True, max_epochs=300)
|
||||
|
||||
default_hooks = dict(
|
||||
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
|
||||
Loading…
x
Reference in New Issue
Block a user