docs: add requirements and renew forms for readmes

mindspore-lab · Nov 9, 2024 · bd1dbd5 · bd1dbd5
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diff --git a/README.md b/README.md
@@ -29,13 +29,13 @@ MindCV is an open-source toolbox for computer vision research and development ba
 
 The following is the corresponding `mindcv` versions and supported `mindspore` versions.
 
-| mindcv | mindspore  |
-|:------:|:----------:|
-|  main  |   master   |
-| v0.4.0 |   2.3.0    |
-| 0.3.0  |   2.2.10   |
-|  0.2   |    2.0     |
-|  0.1   |    1.8     |
+| mindcv |  mindspore  |
+| :----: | :---------: |
+|  main  |   master    |
+| v0.4.0 | 2.3.0/2.3.1 |
+| 0.3.0  |   2.2.10    |
+|  0.2   |     2.0     |
+|  0.1   |     1.8     |
 
 
 ### Major Features

diff --git a/benchmark_results.md b/benchmark_results.md
diff --git a/configs/README.md b/configs/README.md
@@ -33,9 +33,9 @@ Please follow the outline structure and **table format** shown in [densenet/READ
 
 <div align="center">
 
-| model       | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe                                                                                              | download                                                                                                  |
-| ----------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
-| densenet121 | 75.67     | 92.77     | 8.06       | 32         | 8     | 47,34   | O2        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) |
+| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                              | weight                                                                                                    |
+| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
+| densenet121 | 8.06      | 8     | 32         | 224x224    | O2        | 300s          | 47,34   | 5446.81 | 75.67    | 92.77    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) |
 
 </div>
 

diff --git a/configs/bit/README.md b/configs/bit/README.md
@@ -2,6 +2,11 @@
 
 > [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370)
 
+## Requirements
+| mindspore | ascend driver |  firmware   | cann toolkit/kernel |
+| :-------: | :-----------: | :---------: | :-----------------: |
+|   2.3.1   |   24.1.RC2    | 7.3.0.1.231 |    8.0.RC2.beta1    |
+
 ## Introduction
 
 Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision.
@@ -13,23 +18,23 @@ BiT use GroupNorm combined with Weight Standardisation instead of BatchNorm. Sin
 too low. 5) With BiT fine-tuning, good performance can be achieved even if there are only a few examples of each type on natural images.[[1, 2](#References)]
 
 
-## Results
+## Performance
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
-- ascend 910* with graph mode
+- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
 
 *coming soon*
 
-- ascend 910 with graph mode
+- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
 
 
 <div align="center">
 
 
-| model        | top-1 (%) | top-5 (%) | params(M) | batch size | cards | ms/step | jit_level | recipe                                                                                         | download                                                                                |
-| ------------ | --------- | --------- | --------- | ---------- | ----- |---------| --------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
-| bit_resnet50 | 76.81     | 93.17     | 25.55     | 32         | 8     | 74.52   | O2        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) |
+| model name   | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                         | weight                                                                                  |
+| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
+| bit_resnet50 | 25.55     | 8     | 32         | 224x224    | O2        | 146s          | 74.52   | 3413.33 | 76.81    | 93.17    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) |
 
 
 </div>

diff --git a/configs/cmt/README.md b/configs/cmt/README.md
@@ -2,6 +2,11 @@
 
 > [CMT: Convolutional Neural Networks Meet Vision Transformers](https://arxiv.org/abs/2107.06263)
 
+## Requirements
+| mindspore | ascend driver |  firmware   | cann toolkit/kernel |
+| :-------: | :-----------: | :---------: | :-----------------: |
+|   2.3.1   |   24.1.RC2    | 7.3.0.1.231 |    8.0.RC2.beta1    |
+
 ## Introduction
 
 CMT is a method to make full use of the advantages of CNN and transformers so that the model could capture long-range
@@ -10,22 +15,22 @@ and depthwise convolution and pointwise convolution like MobileNet. By combing t
 on ImageNet-1K dataset.
 
 
-## Results
+## Performance
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
-- ascend 910* with graph mode
+- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
 
 *coming soon*
 
-- ascend 910 with graph mode
+- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
 
 <div align="center">
 
 
-| model     | top-1 (%) | top-5 (%) | params(M) | batch size | cards | ms/step | jit_level | recipe                                                                                      | download                                                                             |
-| --------- | --------- | --------- | --------- | ---------- | ----- |---------| --------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
-| cmt_small | 83.24     | 96.41     | 26.09     | 128        | 8     | 500.64  | O2        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) |
+| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                      | weight                                                                               |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
+| cmt_small  | 26.09     | 8     | 128        | 224x224    | O2        | 1268s         | 500.64  | 2048.01 | 83.24    | 96.41    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) |
 
 
 </div>

diff --git a/configs/coat/README.md b/configs/coat/README.md
@@ -2,27 +2,32 @@
 
 > [Co-Scale Conv-Attentional Image Transformers](https://arxiv.org/abs/2104.06399v2)
 
+## Requirements
+| mindspore | ascend driver |  firmware   | cann toolkit/kernel |
+| :-------: | :-----------: | :---------: | :-----------------: |
+|   2.3.1   |   24.1.RC2    | 7.3.0.1.231 |    8.0.RC2.beta1    |
+
 ##  Introduction
 
 Co-Scale Conv-Attentional Image Transformer (CoaT) is a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other. Second, the conv-attentional mechanism is designed by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities.
 
-## Results
+## Performance
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
-- ascend 910* with graph mode
+- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
 
 *coming soon*
 
 
-- ascend 910 with graph mode
+- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
 
 <div align="center">
 
 
-| model     | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe                                                                                       | Weight                                                                                |
-| --------- | --------- | --------- | ---------- | ---------- | ----- |---------| --------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
-| coat_tiny | 79.67     | 94.88     | 5.50       | 32         | 8     | 254.95  | O2        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) |
+| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                       | weight                                                                                |
+| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
+| coat_tiny  | 5.50      | 8     | 32         | 224x224    | O2        | 543s          | 254.95  | 1003.92 | 79.67    | 94.88    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) |
 
 </div>
 

diff --git a/configs/convit/README.md b/configs/convit/README.md
@@ -1,6 +1,11 @@
 # ConViT
 > [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://arxiv.org/abs/2103.10697)
 
+## Requirements
+| mindspore | ascend driver |  firmware   | cann toolkit/kernel |
+| :-------: | :-----------: | :---------: | :-----------------: |
+|   2.3.1   |   24.1.RC2    | 7.3.0.1.231 |    8.0.RC2.beta1    |
+
 ## Introduction
 
 ConViT combines the strengths of convolutional architectures and Vision Transformers (ViTs).
@@ -20,30 +25,30 @@ while offering a much improved sample efficiency.[[1](#references)]
 </p>
 
 
-## Results
+## Performance
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
-- ascend 910* with graph mode
+- Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode
 
 
 <div align="center">
 
 
-| model       | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe                                                                                           | download                                                                                                |
-| ----------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- |
-| convit_tiny | 73.79     | 91.70     | 5.71       | 256        | 8     | 226.51  | O2        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) |
+| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                           | weight                                                                                                  |
+| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- |
+| convit_tiny | 5.71      | 8     | 256        | 224x224    | O2        | 153s          | 226.51  | 9022.03 | 73.79    | 91.70    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) |
 
 </div>
 
-- ascend 910 with graph mode
+- Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode
 
 <div align="center">
 
 
-| model       | top-1 (%) | top-5 (%) | params (M) | batch size | cards | ms/step | jit_level | recipe                                                                                           | download                                                                                  |
-| ----------- | --------- | --------- | ---------- | ---------- | ----- | ------- | --------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- |
-| convit_tiny | 73.66     | 91.72     | 5.71       | 256        | 8     | 231.62  | O2        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) |
+| model name  | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s   | acc@top1 | acc@top5 | recipe                                                                                           | weight                                                                                    |
+| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- |
+| convit_tiny | 5.71      | 8     | 256        | 224x224    | O2        | 133s          | 231.62  | 8827.59 | 73.66    | 91.72    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) |
 
 </div>