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refactor: uniform all model names and correct many other text errors; #701

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Jul 13, 2023
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refactor: uniform all model names and correct many other text errors; #701

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -216,7 +216,7 @@ Currently, MindCV supports the model families listed below. More models with pre
* EfficientNet (MBConvNet Family) https://arxiv.org/abs/1905.11946
* EfficientNet V2 - https://arxiv.org/abs/2104.00298
* GhostNet - https://arxiv.org/abs/1911.11907
* GoogleNet - https://arxiv.org/abs/1409.4842
* GoogLeNet - https://arxiv.org/abs/1409.4842
* Inception-V3 - https://arxiv.org/abs/1512.00567
* Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
* MNASNet - https://arxiv.org/abs/1807.11626
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2 changes: 1 addition & 1 deletion README_CN.md
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Expand Up @@ -217,7 +217,7 @@ python train.py --model=resnet50 --dataset=cifar10 \
* EfficientNet (MBConvNet Family) https://arxiv.org/abs/1905.11946
* EfficientNet V2 - https://arxiv.org/abs/2104.00298
* GhostNet - https://arxiv.org/abs/1911.11907
* GoogleNet - https://arxiv.org/abs/1409.4842
* GoogLeNet - https://arxiv.org/abs/1409.4842
* Inception-V3 - https://arxiv.org/abs/1512.00567
* Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
* MNASNet - https://arxiv.org/abs/1807.11626
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2 changes: 1 addition & 1 deletion RELEASE.md
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Expand Up @@ -123,7 +123,7 @@
`mindcv.models` now expose `num_classes` and `in_channels` as constructor arguments:

- Add DenseNet models and pre-trained weights
- Add GoogleNet models and pre-trained weights
- Add GoogLeNet models and pre-trained weights
- Add Inception V3 models and pre-trained weights
- Add Inception V4 models and pre-trained weights
- Add MnasNet models and pre-trained weights
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196 changes: 100 additions & 96 deletions benchmark_results.md
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2 changes: 1 addition & 1 deletion configs/bit/bit_resnet101_ascend.yaml
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Expand Up @@ -18,7 +18,7 @@ hflip: 0.5
crop_pct: 0.875

# model
model: 'BiTresnet101'
model: 'BiT_resnet101'
num_classes: 1000
pretrained: False
ckpt_path: ''
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2 changes: 1 addition & 1 deletion configs/bit/bit_resnet50_ascend.yaml
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Expand Up @@ -18,7 +18,7 @@ hflip: 0.5
crop_pct: 0.875

# model
model: 'BiTresnet50'
model: 'BiT_resnet50'
num_classes: 1000
pretrained: False
ckpt_path: ''
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2 changes: 1 addition & 1 deletion configs/bit/bit_resnet50x3_ascend.yaml
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Expand Up @@ -20,7 +20,7 @@ crop_pct: 0.875
auto_augment: "randaug-m7-mstd0.5"

# model
model: 'BiTresnet50x3'
model: 'BiT_resnet50x3'
num_classes: 1000
pretrained: False
ckpt_path: ''
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6 changes: 3 additions & 3 deletions configs/convnext/README.md
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Expand Up @@ -25,9 +25,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.

| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|----------------|-----------|-----------|-----------|------------|-------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
| ConvNeXt_tiny | D910x64-G | 81.91 | 95.79 | 28.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) |
| ConvNeXt_small | D910x64-G | 83.40 | 96.36 | 50.22 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_small-e23008f3.ckpt) |
| ConvNeXt_base | D910x64-G | 83.32 | 96.24 | 88.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_base_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_base-ee3544b8.ckpt) |
| convnext_tiny | D910x64-G | 81.91 | 95.79 | 28.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) |
| convnext_small | D910x64-G | 83.40 | 96.36 | 50.22 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_small-e23008f3.ckpt) |
| convnext_base | D910x64-G | 83.32 | 96.24 | 88.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_base_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_base-ee3544b8.ckpt) |

</div>

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6 changes: 3 additions & 3 deletions configs/convnextv2/README.md
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Expand Up @@ -22,9 +22,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.

<div align="center">

| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|-----------------|----------|-----------|-----------|------------|----------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|
| ConvNeXtV2_tiny | D910x8-G | 82.43 | 95.98 | 28.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) |
| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|------------------|----------|-----------|-----------|------------|----------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|
| convnextv2_tiny | D910x8-G | 82.43 | 95.98 | 28.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) |

</div>

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4 changes: 2 additions & 2 deletions configs/crossvit/README.md
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@@ -1,4 +1,4 @@
# Crossvit
# CrossViT
> [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://arxiv.org/abs/2103.14899)

## Introduction
Expand Down Expand Up @@ -77,7 +77,7 @@ python train.py --config configs/crossvit/crossvit_15_ascend.yaml --data_dir /pa
To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`.

```
python validate.py -c configs/crossvit/crossvit15_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
python validate.py -c configs/crossvit/crossvit_15_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
```

### Deployment
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2 changes: 1 addition & 1 deletion configs/crossvit/crossvit_15_ascend.yaml
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Expand Up @@ -28,7 +28,7 @@ crop_pct: 0.935
ema: True

# model
model: 'crossvit15'
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为什么有的数字前有下划线，有的之前没有

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跟ckpt的命名统一，有的ckpt文件名在之前修改的时候就没有加下划线

model: 'crossvit_15'
num_classes: 1000
pretrained: False
ckpt_path: ''
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2 changes: 1 addition & 1 deletion configs/crossvit/crossvit_18_ascend.yaml
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Expand Up @@ -28,7 +28,7 @@ crop_pct: 0.935
ema: True

# model
model: 'crossvit18'
model: 'crossvit_18'
num_classes: 1000
pretrained: False
ckpt_path: ''
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2 changes: 1 addition & 1 deletion configs/crossvit/crossvit_9_ascend.yaml
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Expand Up @@ -27,7 +27,7 @@ color_jitter: 0.4
crop_pct: 0.935

# model
model: 'crossvit9'
model: 'crossvit_9'
num_classes: 1000
pretrained: False
ckpt_path: ''
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12 changes: 6 additions & 6 deletions configs/densenet/README.md
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Expand Up @@ -37,12 +37,12 @@ Our reproduced model performance on ImageNet-1K is reported as follows.

<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) |
| densenet_161 | D910x8-G | 79.09 | 94.66 | 28.90 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_161_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet161-120_5004_Ascend.ckpt) |
| densenet_169 | D910x8-G | 77.26 | 93.71 | 14.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_169_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet169-120_5004_Ascend.ckpt) |
| densenet_201 | D910x8-G | 78.14 | 94.08 | 20.24 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_201_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet201-120_5004_Ascend.ckpt) |
| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|-------------|----------|-----------|-----------|------------|-----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
| densenet121 | 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) |
| densenet161 | D910x8-G | 79.09 | 94.66 | 28.90 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_161_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet161-120_5004_Ascend.ckpt) |
| densenet169 | D910x8-G | 77.26 | 93.71 | 14.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_169_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet169-120_5004_Ascend.ckpt) |
| densenet201 | D910x8-G | 78.14 | 94.08 | 20.24 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_201_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet201-120_5004_Ascend.ckpt) |

</div>

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12 changes: 6 additions & 6 deletions configs/dpn/README.md
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Expand Up @@ -32,12 +32,12 @@ Our reproduced model performance on ImageNet-1K is reported as follows.

<div align="center">

| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|-------|----------|-----------|-----------|------------|------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|
| dpn92 | D910x8-G | 79.46 | 94.49 | 37.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt) |
| dpn98 | D910x8-G | 79.94 | 94.57 | 61.74 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn98_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn98-119a8207.ckpt) |
| dpn107 | D910x8-G | 80.05 | 94.74 | 87.13 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn107_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn107-7d7df07b.ckpt) |
| dpn131 | D910x8-G | 80.07 | 94.72 | 79.48 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn131_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn131-47f084b3.ckpt) |
| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|---------|----------|-----------|-----------|------------|------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|
| dpn92 | D910x8-G | 79.46 | 94.49 | 37.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt) |
| dpn98 | D910x8-G | 79.94 | 94.57 | 61.74 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn98_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn98-119a8207.ckpt) |
| dpn107 | D910x8-G | 80.05 | 94.74 | 87.13 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn107_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn107-7d7df07b.ckpt) |
| dpn131 | D910x8-G | 80.07 | 94.72 | 79.48 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn131_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn131-47f084b3.ckpt) |

</div>

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6 changes: 3 additions & 3 deletions configs/ghostnet/README.md
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Expand Up @@ -29,9 +29,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.

| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|--------------|----------|-----------|-----------|------------|-----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|
| GhostNet_050 | D910x8-G | 66.03 | 86.64 | 2.60 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) |
| GhostNet_100 | D910x8-G | 73.78 | 91.66 | 5.20 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_100_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_100-bef8025a.ckpt) |
| GhostNet_130 | D910x8-G | 75.50 | 92.56 | 7.39 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_130_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_130-cf4c235c.ckpt) |
| ghostnet_050 | D910x8-G | 66.03 | 86.64 | 2.60 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) |
| ghostnet_100 | D910x8-G | 73.78 | 91.66 | 5.20 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_100_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_100-bef8025a.ckpt) |
| ghostnet_130 | D910x8-G | 75.50 | 92.56 | 7.39 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_130_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_130-cf4c235c.ckpt) |

</div>

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4 changes: 2 additions & 2 deletions configs/googlenet/README.md
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Expand Up @@ -14,7 +14,7 @@ training results.[[1](#references)]
<img src="https://user-images.githubusercontent.com/53842165/210749903-5ff23c0e-547f-487d-bb64-70b6e99031ea.jpg" width=180 />
</p>
<p align="center">
<em>Figure 1. Architecture of GoogLENet [<a href="#references">1</a>] </em>
<em>Figure 1. Architecture of GoogLeNet [<a href="#references">1</a>] </em>
</p>

## Results
Expand All @@ -25,7 +25,7 @@ Our reproduced model performance on ImageNet-1K is reported as follows.

| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|-----------|----------|-----------|-----------|------------|---------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|
| GoogLeNet | D910x8-G | 72.68 | 90.89 | 6.99 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) |
| googlenet | D910x8-G | 72.68 | 90.89 | 6.99 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) |

</div>

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4 changes: 2 additions & 2 deletions configs/inceptionv3/README.md
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Expand Up @@ -3,7 +3,7 @@

## Introduction

InceptionV3 is an upgraded version of GoogleNet. One of the most important improvements of V3 is Factorization, which
InceptionV3 is an upgraded version of GoogLeNet. One of the most important improvements of V3 is Factorization, which
decomposes 7x7 into two one-dimensional convolutions (1x7, 7x1), and 3x3 is the same (1x3, 3x1), such benefits, both It
can accelerate the calculation (excess computing power can be used to deepen the network), and can split 1 conv into 2
convs, which further increases the network depth and increases the nonlinearity of the network. It is also worth noting
Expand All @@ -26,7 +26,7 @@ Our reproduced model performance on ImageNet-1K is reported as follows.

| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|--------------|----------|-----------|-----------|------------|---------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|
| Inception_v3 | D910x8-G | 79.11 | 94.40 | 27.20 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v3/inception_v3-38f67890.ckpt) |
| inception_v3 | D910x8-G | 79.11 | 94.40 | 27.20 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v3/inception_v3-38f67890.ckpt) |

</div>

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2 changes: 1 addition & 1 deletion configs/inceptionv4/README.md
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Expand Up @@ -23,7 +23,7 @@ Our reproduced model performance on ImageNet-1K is reported as follows.

| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|--------------|----------|-----------|-----------|------------|---------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|
| Inception_v4 | D910x8-G | 80.88 | 95.34 | 42.74 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v4/inception_v4-db9c45b3.ckpt) |
| inception_v4 | D910x8-G | 80.88 | 95.34 | 42.74 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v4/inception_v4-db9c45b3.ckpt) |

</div>

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