CVers 集合!!!

Image Classification

1. AlexNet

• 论文地址：https://arxiv.org/abs/1404.5997
• 论文详解：https://blog.csdn.net/qq_42735631/article/details/115800315
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/alexnet.py

2. VGGNet

• 论文地址: https://arxiv.org/pdf/1409.1556.pdf
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/116071166
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/vggnet.py

3. GoogLeNet

• 论文地址: http://arxiv.org/abs/1409.4842
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/116404840
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/googlenet.py

4. ResNet

• 论文地址: https://arxiv.org/pdf/1512.03385.pdf
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/116803066
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/resnet.py

5. DenseNet

• 论文地址: https://arxiv.org/pdf/1707.06990.pdf
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/116861074
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/densenet.py

6. MobileNet系列

6.1 MobileNet V1

• 论文地址: https://arxiv.org/abs/1704.04861
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121053364
• 模型代码

6.2 MobileNet V2

• 论文地址: https://arxiv.org/abs/1801.04381
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121053364
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/mobilenetv2.py

6.3 MobileNet V3

• 论文地址: https://arxiv.org/abs/1905.02244
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121053364
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/mobilenetv3.py

7. ShuffleNet系列

7.1 ShuffleNet V1

• 论文地址: https://arxiv.org/abs/1707.01083
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121064514
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/shufflenetv1.py

7.2 ShuffleNet V2

• 论文地址: https://arxiv.org/abs/1807.11164
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121064514
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/shufflenetv2.py

8. GhostNet

• 论文地址: https://arxiv.org/abs/1911.11907
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121065063
• 模型代码: https://github.com/codecat0/CV/blob/main/Image_Classification/models/ghostnet.py

Object Detection

1. R-CNN

• 论文地址: https://arxiv.org/abs/1311.2524
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121147838
• 模型代码

2. Fast R-CNN

• 论文地址: https://arxiv.org/abs/1504.08083
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121151584
• 模型代码

3. Faster R-CNN

• 论文地址: https://arxiv.org/abs/1506.01497
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121164268
• 模型代码: https://github.com/codecat0/CV/tree/main/Object_Detection/Faster_RCNN

4. SSD

• 论文地址: https://arxiv.org/abs/1512.02325
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121331064
• 模型代码: https://github.com/codecat0/CV/tree/main/Object_Detection/SSD

5. YOLO系列

5.1 YOLOV1

• 论文地址: https://arxiv.org/abs/1506.02640
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121445432
• 模型代码

5.2 YOLOV2

• 论文地址: https://arxiv.org/abs/1612.08242
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121456856
• 模型代码

5.3 YOLOV3

• 论文地址: https://arxiv.org/abs/1804.02767
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121479035
• 模型代码: https://github.com/codecat0/CV/tree/main/Object_Detection/YOLOV3

5.3 YOLOV4

• 论文地址: https://arxiv.org/abs/2004.10934
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/121621972
• 模型代码: https://github.com/codecat0/CV/tree/main/Object_Detection/YOLOV4

6. FPN

• 论文地址: https://arxiv.org/abs/1612.03144
• 论文详解
• 模型代码

7. Mask R-CNN

• 论文地址
• 论文详解
• 模型代码

Semantic Segmentation

1. DeepLab系列

• 论文地址
• 论文详解
• 模型代码

2. U-Net

• 论文地址: https://arxiv.org/abs/1505.04597
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/122182266?spm=1001.2014.3001.5501
• 模型代码: https://github.com/codecat0/CV/tree/main/Semantic_Segmentation/UNet

3. FCN

• 论文地址: https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/122247211?spm=1001.2014.3001.5501
• 模型代码: https://github.com/codecat0/CV/tree/main/Semantic_Segmentation/FCN

4. RCF

• 论文地址
• 论文详解
• 模型代码

5. SegNet

• 论文地址: https://arxiv.org/abs/1511.00561
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/122252894?spm=1001.2014.3001.5501
• 模型代码: https://github.com/codecat0/CV/tree/main/Semantic_Segmentation/SegNet

6. PSPNet

• 论文地址：https://arxiv.org/abs/1612.01105
• 论文详解: https://blog.csdn.net/qq_42735631/article/details/122069409
• 模型代码: https://github.com/codecat0/CV/tree/main/Semantic_Segmentation/PSPNet

Generative Models

1. AutoEncoder

• 论文地址
• 论文详解
• 模型代码

2. VAE

• 论文地址
• 论文详解
• 模型代码

3. GAN

• 论文地址
• 论文详解
• 模型代码

4. DCGAN

• 论文地址
• 论文详解
• 模型代码

5. CycleGAN

• 论文地址
• 论文详解
• 模型代码

6. WGAN

• 论文地址
• 论文详解
• 模型代码

7. StyleGAN

• 论文地址
• 论文详解
• 模型代码