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[Feature]Modify modelzoo readme (#230) 

* add mytrain.py for test

* test before layers

* test attr in layers

* test classifier

* delete mytrain.py

* add imagenet_bs4096_AdamW.py

* delete 2 lines of comments

* change bs to 64

* fix bug

* add vit to model_zoo.md

* rename
pull/233/head
whcao 2021-04-29 15:18:55 +08:00 committed by GitHub
parent 16947f1239
commit 3be95b99c2
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5 changed files with 29 additions and 20 deletions
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2
configs/_base_/default_runtime.py
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@ -8,8 +8,6 @@ log_config = dict(
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# You can register your own hooks like this
# custom_hooks=[dict(type='EMAHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'

18
configs/_base_/schedules/imagenet_bs4096_AdamW.py 100644
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@ -0,0 +1,18 @@
# optimizer
optimizer = dict(type='AdamW', lr=0.003, weight_decay=0.3)
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)
runner = dict(type='EpochBasedRunner', max_epochs=300)

6
configs/vision_transformer/vit_base_patch16_224_pretrain_imagenet.py
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@ -1,7 +1,7 @@
_base_ = [
'../_base_/models/vit_base_patch16_224_pretrain.py',
'../_base_/datasets/imagenet_bs32_pil_resize.py',
'../_base_/schedules/imagenet_bs2048_AdamW.py',
'../_base_/datasets/imagenet_bs64_pil_resize.py',
'../_base_/schedules/imagenet_bs4096_AdamW.py',
'../_base_/default_runtime.py'
]
@ -123,7 +123,7 @@ test_pipeline = [
dict(type='Collect', keys=['img'])
]
data = dict(
samples_per_gpu=32,
samples_per_gpu=64,
workers_per_gpu=2,
train=dict(
type=dataset_type,

11
configs/vision_transformer/vit_large_patch16_224_finetune_imagenet.py
View File

@ -8,14 +8,3 @@ _base_ = [
img_norm_cfg = dict(
mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
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(test=dict(pipeline=test_pipeline))

12
docs/model_zoo.md
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@ -20,10 +20,10 @@ The ResNet family models below are trained by standard data augmentations, i.e.,
| ResNet-50 | 25.56 | 4.12 | 76.55 | 93.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.log.json) |
| ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.log.json) |
| ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.log.json) |
| ResNeSt-50 | 27.48 | 5.41 | 81.13 | 95.59 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth) | [log]() |
| ResNeSt-101 | 48.28 | 10.27 | 82.32 | 96.24 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth) | [log]() |
| ResNeSt-200 | 70.2 | 17.53 | 82.41 | 96.22 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth) | [log]() |
| ResNeSt-269 | 110.93 | 22.58 | 82.70 | 96.28 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth) | [log]() |
| ResNeSt-50* | 27.48 | 5.41 | 81.13 | 95.59 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth) | [log]() |
| ResNeSt-101* | 48.28 | 10.27 | 82.32 | 96.24 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth) | [log]() |
| ResNeSt-200* | 70.2 | 17.53 | 82.41 | 96.22 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth) | [log]() |
| ResNeSt-269* | 110.93 | 22.58 | 82.70 | 96.28 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth) | [log]() |
| ResNetV1D-50 | 25.58 | 4.36 | 77.4 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_batch256_imagenet_20200708-1ad0ce94.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_batch256_imagenet_20200708-1ad0ce94.log.json) |
| ResNetV1D-101 | 44.57 | 8.09 | 78.85 | 94.38 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_batch256_imagenet_20200708-9cb302ef.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_batch256_imagenet_20200708-9cb302ef.log.json) |
| ResNetV1D-152 | 60.21 | 11.82 | 79.35 | 94.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_batch256_imagenet_20200708-e79cb6a2.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_batch256_imagenet_20200708-e79cb6a2.log.json) |
@ -36,6 +36,10 @@ The ResNet family models below are trained by standard data augmentations, i.e.,
| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) |
| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) |
| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) | [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) |
| ViT-B/16* | 86.86 | 33.03 | 84.20 | 97.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_base_patch16_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_base_patch16_384.pth) | [log]() |
| ViT-B/32* | 88.3 | 8.56 | 81.73 | 96.13 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_base_patch32_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_base_patch32_384.pth) | [log]() |
| ViT-L/16* | 304.72 | 116.68 | 85.08 | 97.38 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_large_patch16_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_large_patch16_384.pth) | [log]() |
| ViT-L/32* | 306.63 | 29.66 | 81.52 | 96.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit_large_patch32_384_finetune_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit_large_patch32_384.pth) | [log]() |
Models with * are converted from other repos, others are trained by ourselves.