upload mmpretrain

d476eeba · renzhc · 62b8498e · d476eeba · d476eeba · d476eeba
Commit d476eeba authored Sep 02, 2024 by renzhc
20 changed files
--- a/configs/van/van-tiny_8xb128_in1k.py
+++ b/configs/van/van-tiny_8xb128_in1k.py
+_base_ = [
+    '../_base_/models/van/van_tiny.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset setting
+data_preprocessor = dict(
+    mean=[127.5, 127.5, 127.5],
+    std=[127.5, 127.5, 127.5],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+bgr_mean = data_preprocessor['mean'][::-1]
+bgr_std = data_preprocessor['std'][::-1]
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies='timm_increasing',
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=bgr_mean,
+        fill_std=bgr_std),
+    dict(type='PackInputs'),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='ResizeEdge',
+        scale=248,
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='PackInputs'),
+]
+
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline), batch_size=128)
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+
+# schedule settings
+optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
--- a/configs/vgg/README.md
+++ b/configs/vgg/README.md
+# VGG
+
+> [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26739999/142578905-9be586ec-f6fd-4bfb-bbba-432f599d3b9b.png" width="60%"/>
+</div>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+```python
+from mmpretrain import inference_model
+
+predict = inference_model('vgg11_8xb32_in1k', 'demo/bird.JPEG')
+print(predict['pred_class'])
+print(predict['pred_score'])
+```
+
+**Use the model**
+
+```python
+import torch
+from mmpretrain import get_model
+
+model = get_model('vgg11_8xb32_in1k', pretrained=True)
+inputs = torch.rand(1, 3, 224, 224)
+out = model(inputs)
+print(type(out))
+# To extract features.
+feats = model.extract_feat(inputs)
+print(type(feats))
+```
+
+**Train/Test Command**
+
+Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
+
+Train:
+
+```shell
+python tools/train.py configs/vgg/vgg11_8xb32_in1k.py
+```
+
+Test:
+
+```shell
+python tools/test.py configs/vgg/vgg11_8xb32_in1k.py https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth
+```
+
+<!-- [TABS-END] -->
+
+## Models and results
+
+### Image Classification on ImageNet-1k
+
+| Model                |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |             Config              |                                               Download                                               |
+| :------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :-----------------------------: | :--------------------------------------------------------------------------------------------------: |
+| `vgg11_8xb32_in1k`   | From scratch |   132.86   |   7.63    |   68.75   |   88.87   |  [config](vgg11_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.json) |
+| `vgg13_8xb32_in1k`   | From scratch |   133.05   |   11.34   |   70.02   |   89.46   |  [config](vgg13_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.json) |
+| `vgg16_8xb32_in1k`   | From scratch |   138.36   |   15.50   |   71.62   |   90.49   |  [config](vgg16_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.json) |
+| `vgg19_8xb32_in1k`   | From scratch |   143.67   |   19.67   |   72.41   |   90.80   |  [config](vgg19_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.json) |
+| `vgg11bn_8xb32_in1k` | From scratch |   132.87   |   7.64    |   70.67   |   90.16   | [config](vgg11bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.json) |
+| `vgg13bn_8xb32_in1k` | From scratch |   133.05   |   11.36   |   72.12   |   90.66   | [config](vgg13bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.json) |
+| `vgg16bn_8xb32_in1k` | From scratch |   138.37   |   15.53   |   73.74   |   91.66   | [config](vgg16bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.json) |
+| `vgg19bn_8xb32_in1k` | From scratch |   143.68   |   19.70   |   74.68   |   92.27   | [config](vgg19bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.json) |
+
+## Citation
+
+```bibtex
+@article{simonyan2014very,
+  title={Very deep convolutional networks for large-scale image recognition},
+  author={Simonyan, Karen and Zisserman, Andrew},
+  journal={arXiv preprint arXiv:1409.1556},
+  year={2014}
+}
+```
--- a/configs/vgg/metafile.yml
+++ b/configs/vgg/metafile.yml
+Collections:
+  - Name: VGG
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x Xp GPUs
+      Epochs: 100
+      Batch Size: 256
+      Architecture:
+        - VGG
+    Paper:
+      URL: https://arxiv.org/abs/1409.1556
+      Title: "Very Deep Convolutional Networks for Large-Scale Image Recognition"
+    README: configs/vgg/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmpretrain/blob/v0.15.0/mmcls/models/backbones/vgg.py#L39
+      Version: v0.15.0
+
+Models:
+  - Name: vgg11_8xb32_in1k
+    Metadata:
+      FLOPs: 7630000000
+      Parameters: 132860000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 68.75
+          Top 5 Accuracy: 88.87
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth
+    Config: configs/vgg/vgg11_8xb32_in1k.py
+  - Name: vgg13_8xb32_in1k
+    Metadata:
+      FLOPs: 11340000000
+      Parameters: 133050000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 70.02
+          Top 5 Accuracy: 89.46
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth
+    Config: configs/vgg/vgg13_8xb32_in1k.py
+  - Name: vgg16_8xb32_in1k
+    Metadata:
+      FLOPs: 15500000000
+      Parameters: 138360000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 71.62
+          Top 5 Accuracy: 90.49
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth
+    Config: configs/vgg/vgg16_8xb32_in1k.py
+  - Name: vgg19_8xb32_in1k
+    Metadata:
+      FLOPs: 19670000000
+      Parameters: 143670000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 72.41
+          Top 5 Accuracy: 90.8
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth
+    Config: configs/vgg/vgg19_8xb32_in1k.py
+  - Name: vgg11bn_8xb32_in1k
+    Metadata:
+      FLOPs: 7640000000
+      Parameters: 132870000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 70.67
+          Top 5 Accuracy: 90.16
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth
+    Config: configs/vgg/vgg11bn_8xb32_in1k.py
+  - Name: vgg13bn_8xb32_in1k
+    Metadata:
+      FLOPs: 11360000000
+      Parameters: 133050000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 72.12
+          Top 5 Accuracy: 90.66
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth
+    Config: configs/vgg/vgg13bn_8xb32_in1k.py
+  - Name: vgg16bn_8xb32_in1k
+    Metadata:
+      FLOPs: 15530000000
+      Parameters: 138370000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 73.74
+          Top 5 Accuracy: 91.66
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth
+    Config: configs/vgg/vgg16bn_8xb32_in1k.py
+  - Name: vgg19bn_8xb32_in1k
+    Metadata:
+      FLOPs: 19700000000
+      Parameters: 143680000
+    In Collection: VGG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 74.68
+          Top 5 Accuracy: 92.27
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth
+    Config: configs/vgg/vgg19bn_8xb32_in1k.py
--- a/configs/vgg/vgg11_8xb32_in1k.py
+++ b/configs/vgg/vgg11_8xb32_in1k.py
+_base_ = [
+    '../_base_/models/vgg11.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=0.01))
--- a/configs/vgg/vgg11bn_8xb32_in1k.py
+++ b/configs/vgg/vgg11bn_8xb32_in1k.py
+_base_ = [
+    '../_base_/models/vgg11bn.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
--- a/configs/vgg/vgg13_8xb32_in1k.py
+++ b/configs/vgg/vgg13_8xb32_in1k.py
+_base_ = [
+    '../_base_/models/vgg13.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=0.01))
--- a/configs/vgg/vgg13bn_8xb32_in1k.py
+++ b/configs/vgg/vgg13bn_8xb32_in1k.py
+_base_ = [
+    '../_base_/models/vgg13bn.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
--- a/configs/vgg/vgg16_8xb16_voc.py
+++ b/configs/vgg/vgg16_8xb16_voc.py
+_base_ = [
+    '../_base_/datasets/voc_bs16.py',
+    '../_base_/default_runtime.py',
+]
+
+# model settings
+
+# load model pretrained on imagenet
+pretrained = 'https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth'  # noqa
+
+# use different head for multilabel task
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VGG',
+        depth=16,
+        num_classes=20,
+        init_cfg=dict(
+            type='Pretrained', checkpoint=pretrained, prefix='backbone')),
+    neck=None,
+    head=dict(
+        type='MultiLabelClsHead',
+        loss=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
+
+# schedule settings
+optim_wrapper = dict(
+    optimizer=dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0),
+    # update the final linear by 10 times learning rate.
+    paramwise_cfg=dict(custom_keys={'.backbone.classifier': dict(lr_mult=10)}),
+)
+
+# learning policy
+param_scheduler = dict(type='StepLR', by_epoch=True, step_size=20, gamma=0.1)
+
+# train, val, test setting
+train_cfg = dict(by_epoch=True, max_epochs=40, val_interval=1)
+val_cfg = dict()
+test_cfg = dict()
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (8 GPUs) x (16 samples per GPU)
+auto_scale_lr = dict(base_batch_size=128)
--- a/configs/vgg/vgg16_8xb32_in1k.py
+++ b/configs/vgg/vgg16_8xb32_in1k.py
+_base_ = [
+    '../_base_/models/vgg16.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=0.01))
--- a/configs/vgg/vgg16bn_8xb32_in1k.py
+++ b/configs/vgg/vgg16bn_8xb32_in1k.py
+_base_ = [
+    '../_base_/models/vgg16bn.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
--- a/configs/vgg/vgg19_8xb32_in1k.py
+++ b/configs/vgg/vgg19_8xb32_in1k.py
+_base_ = [
+    '../_base_/models/vgg19.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=0.01))
--- a/configs/vgg/vgg19bn_8xb32_in1k.py
+++ b/configs/vgg/vgg19bn_8xb32_in1k.py
+_base_ = [
+    '../_base_/models/vgg19bn.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
--- a/configs/vig/README.md
+++ b/configs/vig/README.md
+# VIG
+
+> [Vision GNN: An Image is Worth Graph of Nodes](https://arxiv.org/abs/2206.00272)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or sequence structure, which is not flexible to capture irregular and complex objects. In this paper, we propose to represent the image as a graph structure and introduce a new Vision GNN (ViG) architecture to extract graph-level feature for visual tasks. We first split the image to a number of patches which are viewed as nodes, and construct a graph by connecting the nearest neighbors. Based on the graph representation of images, we build our ViG model to transform and exchange information among all the nodes. ViG consists of two basic modules: Grapher module with graph convolution for aggregating and updating graph information, and FFN module with two linear layers for node feature transformation. Both isotropic and pyramid architectures of ViG are built with different model sizes. Extensive experiments on image recognition and object detection tasks demonstrate the superiority of our ViG architecture. We hope this pioneering study of GNN on general visual tasks will provide useful inspiration and experience for future research.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26739999/212789461-f085e4da-9ce9-435f-93c0-e1b84d10b79f.png" width="50%"/>
+</div>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+```python
+from mmpretrain import inference_model
+
+predict = inference_model('vig-tiny_3rdparty_in1k', 'demo/bird.JPEG')
+print(predict['pred_class'])
+print(predict['pred_score'])
+```
+
+**Use the model**
+
+```python
+import torch
+from mmpretrain import get_model
+
+model = get_model('vig-tiny_3rdparty_in1k', pretrained=True)
+inputs = torch.rand(1, 3, 224, 224)
+out = model(inputs)
+print(type(out))
+# To extract features.
+feats = model.extract_feat(inputs)
+print(type(feats))
+```
+
+**Test Command**
+
+Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
+
+Test:
+
+```shell
+python tools/test.py configs/vig/vig-tiny_8xb128_in1k.py https://download.openmmlab.com/mmclassification/v0/vig/vig-tiny_3rdparty_in1k_20230117-6414c684.pth
+```
+
+<!-- [TABS-END] -->
+
+## Models and results
+
+### Image Classification on ImageNet-1k
+
+| Model                         |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |                Config                |                                        Download                                        |
+| :---------------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :----------------------------------: | :------------------------------------------------------------------------------------: |
+| `vig-tiny_3rdparty_in1k`\*    | From scratch |    7.18    |   1.31    |   74.40   |   92.34   |  [config](vig-tiny_8xb128_in1k.py)   | [model](https://download.openmmlab.com/mmclassification/v0/vig/vig-tiny_3rdparty_in1k_20230117-6414c684.pth) |
+| `vig-small_3rdparty_in1k`\*   | From scratch |   22.75    |   4.54    |   80.61   |   95.28   |  [config](vig-small_8xb128_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/vig/vig-small_3rdparty_in1k_20230117-5338bf3b.pth) |
+| `vig-base_3rdparty_in1k`\*    | From scratch |   20.68    |   17.68   |   82.62   |   96.04   |  [config](vig-base_8xb128_in1k.py)   | [model](https://download.openmmlab.com/mmclassification/v0/vig/vig-base_3rdparty_in1k_20230117-92f6f12f.pth) |
+| `pvig-tiny_3rdparty_in1k`\*   | From scratch |    9.46    |   1.71    |   78.38   |   94.38   |  [config](pvig-tiny_8xb128_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/vig/pvig-tiny_3rdparty_in1k_20230117-eb77347d.pth) |
+| `pvig-small_3rdparty_in1k`\*  | From scratch |   29.02    |   4.57    |   82.00   |   95.97   | [config](pvig-small_8xb128_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/vig/pvig-small_3rdparty_in1k_20230117-9433dc96.pth) |
+| `pvig-medium_3rdparty_in1k`\* | From scratch |   51.68    |   8.89    |   83.12   |   96.35   | [config](pvig-medium_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vig/pvig-medium_3rdparty_in1k_20230117-21057a6d.pth) |
+| `pvig-base_3rdparty_in1k`\*   | From scratch |   95.21    |   16.86   |   83.59   |   96.52   |  [config](pvig-base_8xb128_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/vig/pvig-base_3rdparty_in1k_20230117-dbab3c85.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch). The config files of these models are only for inference. We haven't reproduce the training results.*
+
+## Citation
+
+```bibtex
+@inproceedings{han2022vig,
+  title={Vision GNN: An Image is Worth Graph of Nodes},
+  author={Kai Han and Yunhe Wang and Jianyuan Guo and Yehui Tang and Enhua Wu},
+  booktitle={NeurIPS},
+  year={2022}
+}
+```
--- a/configs/vig/metafile.yml
+++ b/configs/vig/metafile.yml
+Collections:
+  - Name: VIG
+    Metadata:
+      Training Data: ImageNet-1k
+      Architecture:
+        - Vision GNN
+    Paper:
+      Title: 'Vision GNN: An Image is Worth Graph of Nodes'
+      URL: https://arxiv.org/abs/2206.00272
+    README: configs/vig/README.md
+    Code:
+      URL: null
+      Version: null
+
+Models:
+  - Name: vig-tiny_3rdparty_in1k
+    Metadata:
+      FLOPs: 1309000000
+      Parameters: 7185000
+      Training Data: ImageNet-1k
+    In Collection: VIG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 74.40
+          Top 5 Accuracy: 92.34
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vig/vig-tiny_3rdparty_in1k_20230117-6414c684.pth
+    Config: configs/vig/vig-tiny_8xb128_in1k.py
+    Converted From:
+      Weights: https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/vig/vig_ti_74.5.pth
+      Code: https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
+  - Name: vig-small_3rdparty_in1k
+    Metadata:
+      FLOPs: 4535000000
+      Parameters: 22748000
+      Training Data: ImageNet-1k
+    In Collection: VIG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 80.61
+          Top 5 Accuracy: 95.28
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vig/vig-small_3rdparty_in1k_20230117-5338bf3b.pth
+    Config: configs/vig/vig-small_8xb128_in1k.py
+    Converted From:
+      Weights: https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/vig/vig_s_80.6.pth
+      Code: https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
+  - Name: vig-base_3rdparty_in1k
+    Metadata:
+      FLOPs: 17681000000
+      Parameters: 20685000
+      Training Data: ImageNet-1k
+    In Collection: VIG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.62
+          Top 5 Accuracy: 96.04
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vig/vig-base_3rdparty_in1k_20230117-92f6f12f.pth
+    Config: configs/vig/vig-base_8xb128_in1k.py
+    Converted From:
+      Weights: https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/vig/vig_b_82.6.pth
+      Code: https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
+  - Name: pvig-tiny_3rdparty_in1k
+    Metadata:
+      FLOPs: 1714000000
+      Parameters: 9458000
+      Training Data: ImageNet-1k
+    In Collection: VIG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.38
+          Top 5 Accuracy: 94.38
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vig/pvig-tiny_3rdparty_in1k_20230117-eb77347d.pth
+    Config: configs/vig/pvig-tiny_8xb128_in1k.py
+    Converted From:
+      Weights: https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/pyramid-vig/pvig_ti_78.5.pth.tar
+      Code: https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
+  - Name: pvig-small_3rdparty_in1k
+    Metadata:
+      FLOPs: 4572000000
+      Parameters: 29024000
+      Training Data: ImageNet-1k
+    In Collection: VIG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.00
+          Top 5 Accuracy: 95.97
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vig/pvig-small_3rdparty_in1k_20230117-9433dc96.pth
+    Config: configs/vig/pvig-small_8xb128_in1k.py
+    Converted From:
+      Weights: https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/pyramid-vig/pvig_s_82.1.pth.tar
+      Code: https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
+  - Name: pvig-medium_3rdparty_in1k
+    Metadata:
+      FLOPs: 8886000000
+      Parameters: 51682000
+      Training Data: ImageNet-1k
+    In Collection: VIG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 83.12
+          Top 5 Accuracy: 96.35
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vig/pvig-medium_3rdparty_in1k_20230117-21057a6d.pth
+    Config: configs/vig/pvig-medium_8xb128_in1k.py
+    Converted From:
+      Weights: https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/pyramid-vig/pvig_m_83.1.pth.tar
+      Code: https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
+  - Name: pvig-base_3rdparty_in1k
+    Metadata:
+      FLOPs: 16861000000
+      Parameters: 95213000
+      Training Data: ImageNet-1k
+    In Collection: VIG
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 83.59
+          Top 5 Accuracy: 96.52
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/vig/pvig-base_3rdparty_in1k_20230117-dbab3c85.pth
+    Config: configs/vig/pvig-base_8xb128_in1k.py
+    Converted From:
+      Weights: https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/pyramid-vig/pvig_b_83.66.pth.tar
+      Code: https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch
--- a/configs/vig/pvig-base_8xb128_in1k.py
+++ b/configs/vig/pvig-base_8xb128_in1k.py
+_base_ = [
+    '../_base_/models/vig/pyramid_vig_base.py',
+    '../_base_/datasets/imagenet_bs128_vig_224.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
+
+# dataset settings
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='ResizeEdge',
+        scale=235,
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='PackInputs'),
+]
+
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
--- a/configs/vig/pvig-medium_8xb128_in1k.py
+++ b/configs/vig/pvig-medium_8xb128_in1k.py
+_base_ = [
+    '../_base_/models/vig/pyramid_vig_medium.py',
+    '../_base_/datasets/imagenet_bs128_vig_224.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
--- a/configs/vig/pvig-small_8xb128_in1k.py
+++ b/configs/vig/pvig-small_8xb128_in1k.py
+_base_ = [
+    '../_base_/models/vig/pyramid_vig_small.py',
+    '../_base_/datasets/imagenet_bs128_vig_224.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
--- a/configs/vig/pvig-tiny_8xb128_in1k.py
+++ b/configs/vig/pvig-tiny_8xb128_in1k.py
+_base_ = [
+    '../_base_/models/vig/pyramid_vig_tiny.py',
+    '../_base_/datasets/imagenet_bs128_vig_224.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
--- a/configs/vig/vig-base_8xb128_in1k.py
+++ b/configs/vig/vig-base_8xb128_in1k.py
+_base_ = [
+    '../_base_/models/vig/vig_base.py',
+    '../_base_/datasets/imagenet_bs128_vig_224.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
--- a/configs/vig/vig-small_8xb128_in1k.py
+++ b/configs/vig/vig-small_8xb128_in1k.py
+_base_ = [
+    '../_base_/models/vig/vig_small.py',
+    '../_base_/datasets/imagenet_bs128_vig_224.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]