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| # specific to vit pretrain | ||
| paramwise_cfg = dict(custom_keys={ | ||
| '.cls_token': dict(decay_mult=0.0), | ||
| '.pos_embed': dict(decay_mult=0.0) | ||
| }) | ||
|
|
||
| # optimizer | ||
| optimizer = dict(type='AdamW', lr=0.003, weight_decay=0.3) | ||
| optimizer = dict( | ||
| type='AdamW', | ||
| lr=0.003, | ||
| weight_decay=0.3, | ||
| paramwise_cfg=paramwise_cfg, | ||
| ) | ||
| optimizer_config = dict(grad_clip=dict(max_norm=1.0)) | ||
|
|
||
| # specific to vit pretrain | ||
| paramwise_cfg = dict( | ||
| custom_keys={ | ||
| '.backbone.cls_token': dict(decay_mult=0.0), | ||
| '.backbone.pos_embed': dict(decay_mult=0.0) | ||
| }) | ||
| # learning policy | ||
| lr_config = dict( | ||
| policy='CosineAnnealing', | ||
| min_lr=0, | ||
| warmup='linear', | ||
| warmup_iters=10000, | ||
| warmup_ratio=1e-4) | ||
| warmup_ratio=1e-4, | ||
| ) | ||
| runner = dict(type='EpochBasedRunner', max_epochs=300) |
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| # Training data-efficient image transformers & distillation through attention | ||
| <!-- {DeiT} --> | ||
| <!-- [ALGORITHM] --> | ||
|
|
||
| ## Abstract | ||
|
|
||
| <!-- [ABSTRACT] --> | ||
| Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models. | ||
|
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||
| <!-- [IMAGE] --> | ||
| <div align=center> | ||
| <img src="https://user-images.githubusercontent.com/26739999/143225703-c287c29e-82c9-4c85-a366-dfae30d198cd.png" width="40%"/> | ||
| </div> | ||
|
|
||
| ## Citation | ||
| ```{latex} | ||
| @InProceedings{pmlr-v139-touvron21a, | ||
| title = {Training data-efficient image transformers & distillation through attention}, | ||
| author = {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve}, | ||
| booktitle = {International Conference on Machine Learning}, | ||
| pages = {10347--10357}, | ||
| year = {2021}, | ||
| volume = {139}, | ||
| month = {July} | ||
| } | ||
| ``` | ||
|
|
||
| ## Pretrained models | ||
|
|
||
| The pre-trained models are converted from the [official repo](https://github.com/facebookresearch/deit). And the teacher of the distilled version DeiT is RegNetY-16GF. | ||
|
|
||
| ### ImageNet-1k | ||
|
|
||
| | Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | | ||
| |:---------------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| | ||
| | DeiT-tiny\* | 5.72 | 1.08 | 72.13 | 91.13 | [config](configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_3rdparty_pt-4xb256_in1k_20211124-e930093b.pth) | | ||
| | DeiT-tiny distilled\* | 5.72 | 1.08 | 74.51 | 91.90 | [config](configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211124-e71bdd9a.pth) | | ||
| | DeiT-small\* | 22.05 | 4.24 | 79.83 | 94.95 | [config](configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_3rdparty_pt-4xb256_in1k_20211124-ffe94edd.pth) | | ||
| | DeiT-small distilled\* | 22.05 | 4.24 | 81.17 | 95.40 | [config](configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211124-15e341b0.pth) | | ||
| | DeiT-base\* | 86.57 | 16.86 | 81.79 | 95.59 | [config](configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth) | | ||
| | DeiT-base distilled\* | 86.57 | 16.86 | 83.33 | 96.49 | [config](configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211124-766d123d.pth) | | ||
|
|
||
| *Models with \* are converted from other repos.* | ||
|
|
||
| ## Fine-tuned models | ||
|
|
||
| The fine-tuned models are converted from the [official repo](https://github.com/facebookresearch/deit). | ||
|
|
||
| ### ImageNet-1k | ||
|
|
||
| | Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | | ||
| |:---------------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| | ||
| | DeiT-base 384px\* | 86.86 | 49.37 | 83.04 | 96.31 | [config](configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) | | ||
| | DeiT-base distilled 384px\* | 86.86 | 49.37 | 85.55 | 97.35 | [config](configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211124-91e88933.pth) | | ||
|
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||
| *Models with \* are converted from other repos.* | ||
|
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||
| ```{warning} | ||
| MMClassification doesn't support training the distilled version DeiT. | ||
| And we provide distilled version checkpoints for inference only. | ||
| ``` |
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| _base_ = './deit-base_ft-16xb32_in1k-384px.py' | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| backbone=dict(type='DistilledVisionTransformer'), | ||
| head=dict(type='DeiTClsHead'), | ||
| # Change to the path of the pretrained model | ||
| # init_cfg=dict(type='Pretrained', checkpoint=''), | ||
| ) |
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| _base_ = './deit-small_pt-4xb256_in1k.py' | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| backbone=dict(type='DistilledVisionTransformer', arch='deit-base'), | ||
| head=dict(type='DeiTClsHead', in_channels=768), | ||
| ) | ||
|
|
||
| # data settings | ||
| data = dict(samples_per_gpu=64, workers_per_gpu=5) |
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| _base_ = [ | ||
| '../_base_/datasets/imagenet_bs64_swin_384.py', | ||
| '../_base_/schedules/imagenet_bs4096_AdamW.py', | ||
| '../_base_/default_runtime.py' | ||
| ] | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| type='ImageClassifier', | ||
| backbone=dict( | ||
| type='VisionTransformer', | ||
| arch='deit-base', | ||
| img_size=384, | ||
| patch_size=16, | ||
| ), | ||
| neck=None, | ||
| head=dict( | ||
| type='VisionTransformerClsHead', | ||
| num_classes=1000, | ||
| in_channels=768, | ||
| loss=dict( | ||
| type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), | ||
| ), | ||
| # Change to the path of the pretrained model | ||
| # init_cfg=dict(type='Pretrained', checkpoint=''), | ||
| ) | ||
|
|
||
| # data settings | ||
| data = dict(samples_per_gpu=32, workers_per_gpu=5) |
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| _base_ = './deit-small_pt-4xb256_in1k.py' | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| backbone=dict(type='VisionTransformer', arch='deit-base'), | ||
| head=dict(type='VisionTransformerClsHead', in_channels=768), | ||
| ) | ||
|
|
||
| # data settings | ||
| data = dict(samples_per_gpu=64, workers_per_gpu=5) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,7 @@ | ||
| _base_ = './deit-small_pt-4xb256_in1k.py' | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| backbone=dict(type='DistilledVisionTransformer', arch='deit-small'), | ||
| head=dict(type='DeiTClsHead', in_channels=384), | ||
| ) |
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| _base_ = [ | ||
| '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', | ||
| '../_base_/schedules/imagenet_bs4096_AdamW.py', | ||
| '../_base_/default_runtime.py' | ||
| ] | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| type='ImageClassifier', | ||
| backbone=dict( | ||
| type='VisionTransformer', | ||
| arch='deit-small', | ||
| img_size=224, | ||
| patch_size=16), | ||
| neck=None, | ||
| head=dict( | ||
| type='VisionTransformerClsHead', | ||
| num_classes=1000, | ||
| in_channels=384, | ||
| loss=dict( | ||
| type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), | ||
| ), | ||
| init_cfg=[ | ||
| dict(type='TruncNormal', layer='Linear', std=.02), | ||
| dict(type='Constant', layer='LayerNorm', val=1., bias=0.), | ||
| ]) | ||
|
|
||
| # data settings | ||
| data = dict(samples_per_gpu=256, workers_per_gpu=5) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,7 @@ | ||
| _base_ = './deit-small_pt-4xb256_in1k.py' | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| backbone=dict(type='DistilledVisionTransformer', arch='deit-tiny'), | ||
| head=dict(type='DeiTClsHead', in_channels=192), | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,7 @@ | ||
| _base_ = './deit-small_pt-4xb256_in1k.py' | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| backbone=dict(type='VisionTransformer', arch='deit-tiny'), | ||
| head=dict(type='VisionTransformerClsHead', in_channels=192), | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
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| Collections: | ||
| - Name: DeiT | ||
| Metadata: | ||
| Training Data: ImageNet-1k | ||
| Architecture: | ||
| - Layer Normalization | ||
| - Scaled Dot-Product Attention | ||
| - Attention Dropout | ||
| - Multi-Head Attention | ||
| Paper: | ||
| URL: https://arxiv.org/abs/2012.12877 | ||
| Title: "Training data-efficient image transformers & distillation through attention" | ||
| README: configs/deit/README.md | ||
|
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||
| Models: | ||
| - Name: deit-tiny_3rdparty_pt-4xb256_in1k | ||
| Metadata: | ||
| FLOPs: 1080000000 | ||
| Parameters: 5720000 | ||
| In Collection: DeiT | ||
| Results: | ||
| - Dataset: ImageNet-1k | ||
| Metrics: | ||
| Top 1 Accuracy: 72.13 | ||
| Top 5 Accuracy: 91.13 | ||
| Task: Image Classification | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_3rdparty_pt-4xb256_in1k_20211124-e930093b.pth | ||
| Converted From: | ||
| Weights: https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth | ||
| Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L63 | ||
| Config: configs/deit/deit-tiny_pt-4xb256_in1k.py | ||
| - Name: deit-tiny-distilled_3rdparty_pt-4xb256_in1k | ||
| Metadata: | ||
| FLOPs: 1080000000 | ||
| Parameters: 5720000 | ||
| In Collection: DeiT | ||
| Results: | ||
| - Dataset: ImageNet-1k | ||
| Metrics: | ||
| Top 1 Accuracy: 74.51 | ||
| Top 5 Accuracy: 91.90 | ||
| Task: Image Classification | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211124-e71bdd9a.pth | ||
| Converted From: | ||
| Weights: https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth | ||
| Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L108 | ||
| Config: configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py | ||
| - Name: deit-small_3rdparty_pt-4xb256_in1k | ||
| Metadata: | ||
| FLOPs: 4240000000 | ||
| Parameters: 22050000 | ||
| In Collection: DeiT | ||
| Results: | ||
| - Dataset: ImageNet-1k | ||
| Metrics: | ||
| Top 1 Accuracy: 79.83 | ||
| Top 5 Accuracy: 94.95 | ||
| Task: Image Classification | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-small_3rdparty_pt-4xb256_in1k_20211124-ffe94edd.pth | ||
| Converted From: | ||
| Weights: https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth | ||
| Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L78 | ||
| Config: configs/deit/deit-small_pt-4xb256_in1k.py | ||
| - Name: deit-small-distilled_3rdparty_pt-4xb256_in1k | ||
| Metadata: | ||
| FLOPs: 4240000000 | ||
| Parameters: 22050000 | ||
| In Collection: DeiT | ||
| Results: | ||
| - Dataset: ImageNet-1k | ||
| Metrics: | ||
| Top 1 Accuracy: 81.17 | ||
| Top 5 Accuracy: 95.40 | ||
| Task: Image Classification | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211124-15e341b0.pth | ||
| Converted From: | ||
| Weights: https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth | ||
| Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L123 | ||
| Config: configs/deit/deit-small-distilled_pt-4xb256_in1k.py | ||
| - Name: deit-base_3rdparty_pt-16xb64_in1k | ||
| Metadata: | ||
| FLOPs: 16860000000 | ||
| Parameters: 86570000 | ||
| In Collection: DeiT | ||
| Results: | ||
| - Dataset: ImageNet-1k | ||
| Metrics: | ||
| Top 1 Accuracy: 81.79 | ||
| Top 5 Accuracy: 95.59 | ||
| Task: Image Classification | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth | ||
| Converted From: | ||
| Weights: https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth | ||
| Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L93 | ||
| Config: configs/deit/deit-base_pt-16xb64_in1k.py | ||
| - Name: deit-base-distilled_3rdparty_pt-16xb64_in1k | ||
| Metadata: | ||
| FLOPs: 16860000000 | ||
| Parameters: 86570000 | ||
| In Collection: DeiT | ||
| Results: | ||
| - Dataset: ImageNet-1k | ||
| Metrics: | ||
| Top 1 Accuracy: 83.33 | ||
| Top 5 Accuracy: 96.49 | ||
| Task: Image Classification | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211124-766d123d.pth | ||
| Converted From: | ||
| Weights: https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth | ||
| Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L138 | ||
| Config: configs/deit/deit-base-distilled_pt-16xb64_in1k.py | ||
| - Name: deit-base_3rdparty_ft-16xb32_in1k-384px | ||
| Metadata: | ||
| FLOPs: 49370000000 | ||
| Parameters: 86860000 | ||
| In Collection: DeiT | ||
| Results: | ||
| - Dataset: ImageNet-1k | ||
| Metrics: | ||
| Top 1 Accuracy: 83.04 | ||
| Top 5 Accuracy: 96.31 | ||
| Task: Image Classification | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth | ||
| Converted From: | ||
| Weights: https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth | ||
| Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L153 | ||
| Config: configs/deit/deit-base_ft-16xb32_in1k-384px.py | ||
| - Name: deit-base-distilled_3rdparty_ft-16xb32_in1k-384px | ||
| Metadata: | ||
| FLOPs: 49370000000 | ||
| Parameters: 86860000 | ||
| In Collection: DeiT | ||
| Results: | ||
| - Dataset: ImageNet-1k | ||
| Metrics: | ||
| Top 1 Accuracy: 85.55 | ||
| Top 5 Accuracy: 97.35 | ||
| Task: Image Classification | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211124-91e88933.pth | ||
| Converted From: | ||
| Weights: https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth | ||
| Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L168 | ||
| Config: configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py |
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