Merge pull request #1 from AgentMaker/main

Add the PVT model
New version 1.0.6
Update codes

Author: jm12138

README.md

@@ -8,6 +8,8 @@ English | [简体中文](README_CN.md)
 
 A PaddlePaddle version image model zoo.
 
+![](https://ai-studio-static-online.cdn.bcebos.com/34e7bbbc80d24412b3c21efb56778ad43b53f9b1be104e499e0ff8b663a64a53)
+
 ## Install Package
 * Install by pip：
 
@@ -22,10 +24,10 @@ A PaddlePaddle version image model zoo.
 
     ```python
     import paddle
-    from ppim import rednet26
+    from ppim import rednet_26
 
     # Load the model
-    model, val_transforms = rednet26(pretrained=True)
+    model, val_transforms = rednet_26(pretrained=True)
 
     # Model summary 
     paddle.summary(model, input_size=(1, 3, 224, 224))
@@ -45,10 +47,10 @@ A PaddlePaddle version image model zoo.
     import paddle.vision.transforms as T
     from paddle.vision import Cifar100
 
-    from ppim import rexnet_100
+    from ppim import rexnet_1_0
 
     # Load the model
-    model, val_transforms = rexnet_100(pretrained=True)
+    model, val_transforms = rexnet_1_0(pretrained=True)
 
     # Use the PaddleHapi Model
     model = paddle.Model(model)
@@ -95,161 +97,24 @@ A PaddlePaddle version image model zoo.
     ```
 
 ## Model Zoo
-### ReXNet
-* Paper：[ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network](https://arxiv.org/abs/2007.00992)
-* Origin Repo：[clovaai/rexnet](https://github.com/clovaai/rexnet)
-* Evaluate Transforms：
-
-    ```python
-    # backend: pil
-    # input_size: 224x224
-    transforms = T.Compose([
-        T.Resize(256, interpolation='bicubic'),
-        T.CenterCrop(224),
-        T.ToTensor(),
-        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
-    ```
 
-* Model Details：
+* [DLA](./docs/en/model_zoo/dla.md)
 
-    |         Model         | Params(M) | FLOPs(G) | Top-1 (%) | Top-5 (%) |
-    |:---------------------:|:---------:|:--------:|:---------:|:---------:|
-    | ReXNet-1.0             |  4.8 | 0.40 | 77.9 | 93.9 |
-    | ReXNet-1.3             |  7.6 | 0.66 | 79.5 | 94.7 |
-    | ReXNet-1.5             |  7.6 | 0.66 | 80.3 | 95.2 |
-    | ReXNet-2.0             |  16  | 1.5  | 81.6 | 95.7 |
-    | ReXNet-3.0             |  34  | 3.4  | 82.8 | 96.2 |
+* [ReXNet](./docs/en/model_zoo/rexnet.md)
 
-### RedNet
-* Paper：[Involution: Inverting the Inherence of Convolution for Visual Recognition](https://arxiv.org/abs/2103.06255)
-* Origin Repo：[d-li14/involution](https://github.com/d-li14/involution)
-* Evaluate Transforms：
+* [RedNet](./docs/en/model_zoo/rednet.md)
 
-    ```python
-    # backend: cv2
-    # input_size: 224x224
-    transforms = T.Compose([
-        T.Resize(256),
-        T.CenterCrop(224),
-        T.Normalize(
-            mean=[123.675, 116.28, 103.53],
-            std=[58.395, 57.12, 57.375],
-            to_rgb=True,
-            data_format='HWC'
-        ),
-        T.ToTensor(),
-    ])
-    ```
+* [RepVGG](./docs/en/model_zoo/repvgg.md)
 
-* Model Details：
+* [HarDNet](./docs/en/model_zoo/hardnet.md)
 
-    |         Model         | Params(M) | FLOPs(G) | Top-1 (%) | Top-5 (%) |
-    |:---------------------:|:---------:|:--------:|:---------:|:---------:|
-    | RedNet-26             |  9.23 | 1.73 | 75.96 | 93.19 |
-    | RedNet-38             | 12.39 | 2.22 | 77.48 | 93.57 |
-    | RedNet-50             | 15.54 | 2.71 | 78.35 | 94.13 |
-    | RedNet-101            | 25.65 | 4.74 | 78.92 | 94.35 |
-    | RedNet-152            | 33.99 | 6.79 | 79.12 | 94.38 |
-
-### RepVGG
-* Paper：[RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697)
-* Origin Repo：[DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
-* Evaluate Transforms：
-
-    ```python
-    # backend: pil
-    # input_size: 224x224
-    transforms = T.Compose([
-        T.Resize(256),
-        T.CenterCrop(224),
-        T.ToTensor(),
-        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
-    ```
-
-* Model Details：
-
-    |         Model         | Params(M) | FLOPs(G) | Top-1 (%) | Top-5 (%) |
-    |:---------------------:|:---------:|:--------:|:---------:|:---------:|
-    | RepVGG-A0             |  8.30  | 1.4  | 72.41 |       |
-    | RepVGG-A1             | 12.78  | 2.4  | 74.46 |       |
-    | RepVGG-A2             | 25.49  | 5.1  | 76.48 |       |
-    | RepVGG-B0             | 14.33  | 3.1  | 75.14 |       |
-    | RepVGG-B1             | 51.82  | 11.8 | 78.37 |       |
-    | RepVGG-B2             | 80.31  | 18.4 | 78.78 |       |
-    | RepVGG-B3             | 110.96 | 26.2 | 80.52 |       |
-    | RepVGG-B1g2           | 41.36  | 8.8  | 77.78 |       |
-    | RepVGG-B1g4           | 36.12  | 7.3  | 77.58 |       |
-    | RepVGG-B2g4           | 55.77  | 11.3 | 79.38 |       |
-    | RepVGG-B3g4           | 75.62  | 16.1 | 80.21 |       |
-
-### PiT
-* Paper：[Rethinking Spatial Dimensions of Vision Transformers](https://arxiv.org/abs/2103.16302)
-* Origin Repo：[naver-ai/pit](https://github.com/naver-ai/pit)
-* Evaluate Transforms：
-
-    ```python
-    # backend: pil
-    # input_size: 224x224
-    transforms = T.Compose([
-        T.Resize(248, interpolation='bicubic'),
-        T.CenterCrop(224),
-        T.ToTensor(),
-        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
-    ```
-
-* Model Details：
-
-    |         Model         | Params(M) | FLOPs(G) | Top-1 (%) | Top-5 (%) |
-    |:---------------------:|:---------:|:--------:|:---------:|:---------:|
-    | PiT-Ti            | 4.9  | 0.71 | 73.0 |    |
-    | PiT-XS            | 10.6 | 1.4  | 78.1 |    |
-    | PiT-S             | 23.5 | 2.9  | 80.9 |    |
-    | PiT-B             | 73.8 | 12.5 | 82.0 |    |
-    | PiT-Ti distilled  | 4.9  | 0.71 | 74.6 |    |
-    | PiT-XS distilled  | 10.6 | 1.4  | 79.1 |    |
-    | PiT-S distilled   | 23.5 | 2.9  | 81.9 |    |
-    | PiT-B distilled   | 73.8 | 12.5 | 84.0 |    |
-
-### DeiT
-* Paper：[Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877)
-* Origin Repo：[facebookresearch/deit](https://github.com/facebookresearch/deit)
-* Evaluate Transforms：
-
-    ```python
-    # backend: pil
-    # input_size: 224x224
-    transforms = T.Compose([
-        T.Resize(248, interpolation='bicubic'),
-        T.CenterCrop(224),
-        T.ToTensor(),
-        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
+* [PiT](./docs/en/model_zoo/pit.md)
 
-    # backend: pil
-    # input_size: 384x384
-    transforms = T.Compose([
-        T.Resize(384, interpolation='bicubic'),
-        T.CenterCrop(384),
-        T.ToTensor(),
-        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
-    ```
+* [PVT](./docs/en/model_zoo/pvt.md)
 
-* Model Details：
+* [TNT](./docs/en/model_zoo/tnt.md)
 
-    |         Model         | Params(M) | FLOPs(G) | Top-1 (%) | Top-5 (%) |
-    |:---------------------:|:---------:|:--------:|:---------:|:---------:|
-    | DeiT-tiny               |  5  | | 72.2 |  91.1     |
-    | DeiT-small              | 22  | | 79.9 |  95.0     |
-    | DeiT-base               | 86  | | 81.8 |  95.6     |
-    | DeiT-tiny distilled     | 6   | | 74.5 |  91.9     |
-    | DeiT-small distilled    | 22  | | 81.2 |  95.4     |
-    | DeiT-base distilled     | 87  | | 83.4 |  96.5     |
-    | DeiT-base 384           | 87  | | 82.9 |  96.2     |
-    | DeiT-base distilled 384 | 88  | | 85.2 |  97.2     |
+* [DeiT](./docs/en/model_zoo/deit.md)
 
 ## Citation
 ```
@@ -259,37 +124,78 @@ A PaddlePaddle version image model zoo.
     journal = {arXiv preprint arXiv:2007.00992},
     year = {2020},
 }
-```
-```
+
 @InProceedings{Li_2021_CVPR,
     title = {Involution: Inverting the Inherence of Convolution for Visual Recognition},
     author = {Li, Duo and Hu, Jie and Wang, Changhu and Li, Xiangtai and She, Qi and Zhu, Lei and Zhang, Tong and Chen, Qifeng},
     booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     month = {June},
     year = {2021}
 }
-```
-```
+
 @article{ding2021repvgg,
     title={RepVGG: Making VGG-style ConvNets Great Again},
     author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian},
     journal={arXiv preprint arXiv:2101.03697},
     year={2021}
 }
-```
-```
+
 @article{heo2021pit,
     title={Rethinking Spatial Dimensions of Vision Transformers},
     author={Byeongho Heo and Sangdoo Yun and Dongyoon Han and Sanghyuk Chun and Junsuk Choe and Seong Joon Oh},
     journal={arXiv: 2103.16302},
     year={2021},
 }
-```
-```
+
 @article{touvron2020deit,
     title = {Training data-efficient image transformers & distillation through attention},
     author = {Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Herv'e J'egou},
     journal = {arXiv preprint arXiv:2012.12877},
     year = {2020}
 }
-```
+
+@misc{han2021transformer,
+    title={Transformer in Transformer}, 
+    author={Kai Han and An Xiao and Enhua Wu and Jianyuan Guo and Chunjing Xu and Yunhe Wang},
+    year={2021},
+    eprint={2103.00112},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+
+@misc{chao2019hardnet,
+    title={HarDNet: A Low Memory Traffic Network}, 
+    author={Ping Chao and Chao-Yang Kao and Yu-Shan Ruan and Chien-Hsiang Huang and Youn-Long Lin},
+    year={2019},
+    eprint={1909.00948},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+
+@misc{yu2019deep,
+    title={Deep Layer Aggregation}, 
+    author={Fisher Yu and Dequan Wang and Evan Shelhamer and Trevor Darrell},
+    year={2019},
+    eprint={1707.06484},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+
+@misc{dosovitskiy2020image,
+    title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, 
+    author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
+    year={2020},
+    eprint={2010.11929},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+
+@misc{wang2021pyramid,
+      title={Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions}, 
+      author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and Tong Lu and Ping Luo and Ling Shao},
+      year={2021},
+      eprint={2102.12122},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```