docs: renew readmes, add ms/step data to forms and remove development docs (#812)

Co-authored-by: ChongWei905 <weichong4@huawei.com>

Author: ChongWei905

README.md

@@ -217,7 +217,6 @@ We provide the following jupyter notebook tutorials to help users learn to use M
 - [Finetune a pretrained model on custom datasets](docs/en/tutorials/finetune.md)
 - [Customize your model]() //coming soon
 - [Optimizing performance for vision transformer]() //coming soon
-- [Deployment demo](docs/en/tutorials/deployment.md)
 
 ## Model List
 

README_CN.md

@@ -121,7 +121,7 @@ python infer.py --model=swin_tiny --image_path='./dog.jpg'
 
     ```shell
     # 分布式训练
-    # 假设你有4张GPU或者NPU卡
+    # 假设你有4张NPU卡
     msrun --bind_core=True --worker_num 4 python train.py --distribute \
         --model densenet121 --dataset imagenet --data_dir ./datasets/imagenet
     ```

benchmark_results.md

@@ -33,17 +33,20 @@ Please follow the outline structure and **table format** shown in [densenet/READ
 
 <div align="center">
 
-| Model        | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                              | Download                                                                                           |
-|--------------|----------|-----------|-----------|------------|-----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
-| densenet_121 | D910x8-G | 75.64     | 92.84     | 8.06       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet121-120_5004_Ascend.ckpt) |
+| 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) |
 
 </div>
 
 Illustration:
 - Model: model name in lower case with _ seperator.
-- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
 - Top-1 and Top-5: Accuracy reported on the validatoin set of ImageNet-1K. Keep 2 digits after the decimal point.
 - Params (M): # of model parameters in millions (10^6). Keep **2 digits** after the decimal point
+- Batch Size: Training batch size
+- Cards: # of cards
+- Ms/step: Time used on training per step in ms
+- Jit_level: Jit level of mindspore context, which contains 3 levels: O0/O1/O2
 - Recipe: Training recipe/configuration linked to a yaml config file.
 - Download: url of the pretrained model weights
 
@@ -62,10 +65,10 @@ Illustration:
 For consistency, it is recommended to provide distributed training commands based on `msrun --bind_core=True --worker_num {num_devices} python train.py`, instead of using shell script such as `distrubuted_train.sh`.
 
   ```shell
-  # standalone training on a gpu or ascend device
+  # standalone training on single NPU device
   python train.py --config configs/densenet/densenet_121_gpu.yaml --data_dir /path/to/dataset --distribute False
 
-  # distributed training on gpu or ascend divices
+  # distributed training on NPU divices
   msrun --bind_core=True --worker_num 8 python train.py --config configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/imagenet
 
   ```

configs/README.md

@@ -17,25 +17,24 @@ too low. 5) With BiT fine-tuning, good performance can be achieved even if there
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
-performance tested on ascend 910*(8p) with graph mode
+- ascend 910* with graph mode
 
 *coming soon*
 
-performance tested on ascend 910(8p) with graph mode
+- ascend 910 with graph mode
 
 
 <div align="center">
 
-| Model        | Top-1 (%) | Top-5 (%) | Params(M) | Batch Size | Recipe                                                                                         | Download                                                                                |
-| ------------ | --------- | --------- | --------- | ---------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
-| bit_resnet50 | 76.81     | 93.17     | 25.55     | 32         | [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        | 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) |
 
 
 </div>
 
 #### Notes
-
-- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
 - Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.
 
 ## Quick Start
@@ -44,7 +43,7 @@ performance tested on ascend 910(8p) with graph mode
 
 #### Installation
 
-Please refer to the [installation instruction](https://github.com/mindspore-lab/mindcv#installation) in MindCV.
+Please refer to the [installation instruction](https://mindspore-lab.github.io/mindcv/installation/) in MindCV.
 
 #### Dataset Preparation
 
@@ -57,11 +56,10 @@ Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/201
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple NPU devices
 msrun --bind_core=True --worker_num 8 python train.py --config configs/bit/bit_resnet50_ascend.yaml --data_dir /path/to/imagenet
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above `msrun` command.
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py).
 
@@ -72,7 +70,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on single NPU device
 python train.py --config configs/bit/bit_resnet50_ascend.yaml --data_dir /path/to/dataset --distribute False
 ```
 
@@ -84,10 +82,6 @@ To validate the accuracy of the trained model, you can use `validate.py` and par
 python validate.py -c configs/bit/bit_resnet50_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
 ```
 
-### Deployment
-
-Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/tutorials/deployment/) in MindCV.
-
 ## References
 
 <!--- Guideline: Citation format should follow GB/T 7714. -->

configs/bit/README.md

@@ -14,24 +14,23 @@ on ImageNet-1K dataset.
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
-performance tested on ascend 910*(8p) with graph mode
+- ascend 910* with graph mode
 
 *coming soon*
 
-performance tested on ascend 910(8p) with graph mode
+- ascend 910 with graph mode
 
 <div align="center">
 
-| Model     | Top-1 (%) | Top-5 (%) | Params(M) | Batch Size | Recipe                                                                                      | Download                                                                             |
-| --------- | --------- | --------- | --------- | ---------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
-| cmt_small | 83.24     | 96.41     | 26.09     | 128        | [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     | 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) |
 
 
 </div>
 
 #### Notes
-
-- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
 - Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.
 
 ## Quick Start
@@ -40,7 +39,7 @@ performance tested on ascend 910(8p) with graph mode
 
 #### Installation
 
-Please refer to the [installation instruction](https://github.com/mindspore-lab/mindcv#installation) in MindCV.
+Please refer to the [installation instruction](https://mindspore-lab.github.io/mindcv/installation/) in MindCV.
 
 #### Dataset Preparation
 
@@ -53,11 +52,10 @@ Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/201
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple NPU devices
 msrun --bind_core=True --worker_num 8 python train.py --config configs/cmt/cmt_small_ascend.yaml --data_dir /path/to/imagenet
 ```
 
-Similarly, you can train the model on multiple GPU devices with the above `msrun` command.
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py).
 
@@ -68,7 +66,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on single NPU device
 python train.py --config configs/cmt/cmt_small_ascend.yaml --data_dir /path/to/dataset --distribute False
 ```
 
@@ -80,10 +78,6 @@ To validate the accuracy of the trained model, you can use `validate.py` and par
 python validate.py -c configs/cmt/cmt_small_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
 ```
 
-### Deployment
-
-Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/tutorials/deployment/).
-
 ## References
 
 <!--- Guideline: Citation format should follow GB/T 7714. -->

configs/cmt/README.md

@@ -10,23 +10,23 @@ Co-Scale Conv-Attentional Image Transformer (CoaT) is a Transformer-based image
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
-performance tested on ascend 910*(8p) with graph mode
+- ascend 910* with graph mode
 
 *coming soon*
 
 
-performance tested on ascend 910(8p) with graph mode
+- ascend 910 with graph mode
 
 <div align="center">
 
-| Model     | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                       | Weight                                                                                |
-| --------- | --------- | --------- | ---------- | ---------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
-| coat_tiny | 79.67     | 94.88     | 5.50       | 32         | [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     | 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) |
 
 </div>
 
 #### Notes
-- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
 - Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.
 
 
@@ -35,7 +35,7 @@ performance tested on ascend 910(8p) with graph mode
 ### Preparation
 
 #### Installation
-Please refer to the [installation instruction](https://github.com/mindspore-lab/mindcv#installation) in MindCV.
+Please refer to the [installation instruction](https://mindspore-lab.github.io/mindcv/installation/) in MindCV.
 
 #### Dataset Preparation
 Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation.
@@ -47,12 +47,11 @@ Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/201
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
 
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple NPU devices
 msrun --bind_core=True --worker_num 8 python train.py --config configs/coat/coat_lite_tiny_ascend.yaml --data_dir /path/to/imagenet
 ```
 
 
-Similarly, you can train the model on multiple GPU devices with the above `msrun` command.
 
 For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py).
 
@@ -63,7 +62,7 @@ For detailed illustration of all hyper-parameters, please refer to [config.py](h
 If you want to train or finetune the model on a smaller dataset without distributed training, please run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on single NPU device
 python train.py --config configs/coat/coat_lite_tiny_ascend.yaml --data_dir /path/to/dataset --distribute False
 ```
 
@@ -75,10 +74,6 @@ To validate the accuracy of the trained model, you can use `validate.py` and par
 python validate.py -c configs/coat/coat_lite_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
 ```
 
-### Deployment
-
-To deploy online inference services with the trained model efficiently, please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/tutorials/deployment/).
-
 ## References
 
 [1] Han D, Yun S, Heo B, et al. Rethinking channel dimensions for efficient model design[C]//Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition. 2021: 732-741.