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| Original file line number | Diff line number | Diff line change |
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| Collections: | ||
| - Name: Vision Transformer | ||
| Metadata: | ||
| Architecture: | ||
| - Attention Dropout | ||
| - Convolution | ||
| - Dense Connections | ||
| - Dropout | ||
| - GELU | ||
| - Layer Normalization | ||
| - Multi-Head Attention | ||
| - Scaled Dot-Product Attention | ||
| - Tanh Activation | ||
| Paper: | ||
| URL: https://arxiv.org/pdf/2010.11929.pdf | ||
| Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale' | ||
| README: configs/swin_transformer/README.md | ||
|
|
||
| Models: | ||
| - Name: vit-base-p16_in21k-pre-3rdparty_in1k-384 | ||
| In Collection: Vision Transformer | ||
| Config: configs/vision_transformer/vit-base-p16_ft-evalonly_in-1k-384.py | ||
| Metadata: | ||
| FLOPs: 33030000000 | ||
| Parameters: 86860000 | ||
| Training Data: ImageNet | ||
| Results: | ||
| - Dataset: ImageNet | ||
| Task: Image Classification | ||
| Metrics: | ||
| Top 1 Accuracy: 85.43 | ||
| Top 5 Accuracy: 97.77 | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_in1k-384_20210819-65c4bf44.pth | ||
| Converted From: | ||
| Weights: https://console.cloud.google.com/storage/browser/_details/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz | ||
| Code: https://github.com/google-research/vision_transformer/blob/88a52f8892c80c10de99194990a517b4d80485fd/vit_jax/models.py#L208 | ||
| - Name: vit-base-p32_in21k-pre-3rdparty_in1k-384 | ||
| In Collection: Vision Transformer | ||
| Config: configs/vision_transformer/vit-base-p32_ft-evalonly_in-1k-384.py | ||
| Metadata: | ||
| FLOPs: 8560000000 | ||
| Parameters: 88300000 | ||
| Training Data: ImageNet | ||
| Results: | ||
| - Dataset: ImageNet | ||
| Task: Image Classification | ||
| Metrics: | ||
| Top 1 Accuracy: 84.01 | ||
| Top 5 Accuracy: 97.08 | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_in1k-384_20210819-a56f8886.pth | ||
| Converted From: | ||
| Weights: https://console.cloud.google.com/storage/browser/_details/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz | ||
| Code: https://github.com/google-research/vision_transformer/blob/88a52f8892c80c10de99194990a517b4d80485fd/vit_jax/models.py#L208 | ||
| - Name: vit-large-p16_in21k-pre-3rdparty_in1k-384 | ||
| In Collection: Vision Transformer | ||
| Config: configs/vision_transformer/vit-large-p16_ft-evalonly_in-1k-384.py | ||
| Metadata: | ||
| FLOPs: 116680000000 | ||
| Parameters: 304720000 | ||
| Training Data: ImageNet | ||
| Results: | ||
| - Dataset: ImageNet | ||
| Task: Image Classification | ||
| Metrics: | ||
| Top 1 Accuracy: 85.63 | ||
| Top 5 Accuracy: 97.63 | ||
| Weights: https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_in1k-384_20210819-0bb8550c.pth | ||
| Converted From: | ||
| Weights: https://console.cloud.google.com/storage/browser/_details/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_strong1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz | ||
| Code: https://github.com/google-research/vision_transformer/blob/88a52f8892c80c10de99194990a517b4d80485fd/vit_jax/models.py#L208 |
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configs/vision_transformer/vit-large-p32_ft-evalonly_in-1k-384.py
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| # Refer to pytorch-image-models | ||
| _base_ = [ | ||
| '../_base_/models/vit-large-p32.py', | ||
| '../_base_/datasets/imagenet_bs32_pil_resize.py', | ||
| '../_base_/schedules/imagenet_bs256_epochstep.py', | ||
| '../_base_/default_runtime.py' | ||
| ] | ||
|
|
||
| model = dict(backbone=dict(img_size=384)) | ||
|
|
||
| img_norm_cfg = dict( | ||
| mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='RandomResizedCrop', size=384, backend='pillow'), | ||
| dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='ToTensor', keys=['gt_label']), | ||
| dict(type='Collect', keys=['img', 'gt_label']) | ||
| ] | ||
|
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||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='Resize', size=(384, -1), backend='pillow'), | ||
| dict(type='CenterCrop', crop_size=384), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']) | ||
| ] | ||
|
|
||
| data = dict( | ||
| train=dict(pipeline=train_pipeline), test=dict(pipeline=test_pipeline)) |
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