[Model] Add IPU ViT model

akashlp27 · Jun 3, 2022 · 58b21ee · 58b21ee
1 parent 313d357
commit 58b21ee
Showing 1 changed file with 6 additions and 5 deletions.
diff --git a/configs/vision_transformer/README.md b/configs/vision_transformer/README.md
@@ -34,11 +34,12 @@ The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don'
 
 ### ImageNet-1k
 
-|   Model   |   Pretrain   | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                               Config                               |                               Download                               |
-| :-------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------: | :------------------------------------------------------------------: |
-| ViT-B16\* | ImageNet-21k |  384x384   |   86.86   |  33.03   |   85.43   |   97.77   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth) |
-| ViT-B32\* | ImageNet-21k |  384x384   |   88.30   |   8.56   |   84.01   |   97.08   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth) |
-| ViT-L16\* | ImageNet-21k |  384x384   |  304.72   |  116.68  |   85.63   |   97.63   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth) |
+|     Model     |   Pretrain   | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                              Config                              |                              Download                              |
+| :-----------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------: | :----------------------------------------------------------------: |
+|   ViT-B16\*   | ImageNet-21k |  384x384   |   86.86   |  33.03   |   85.43   |   97.77   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth) |
+|   ViT-B32\*   | ImageNet-21k |  384x384   |   88.30   |   8.56   |   84.01   |   97.08   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth) |
+|   ViT-L16\*   | ImageNet-21k |  384x384   |  304.72   |  116.68  |   85.63   |   97.63   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth) |
+| ViT-B16 (IPU) | ImageNet-21k |  224x224   |   86.86   |  33.03   |   81.22   |   95.56   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-4xb544-ipu_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit-base-p16_ft-4xb544-ipu_in1k_20220603-c215811a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vit/vit-base-p16_ft-4xb544-ipu_in1k.log) |
 
 *Models with * are converted from the [official repo](https://github.com/google-research/vision_transformer#available-vit-models). 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.*