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A tensorflow2 implementation of some basic CNNs.

• MobileNet_V1
• MobileNet_V2
• MobileNet_V3
• EfficientNet
• ResNeXt
• InceptionV4, InceptionResNetV1, InceptionResNetV2
• SE_ResNet_50, SE_ResNet_101, SE_ResNet_152, SE_ResNeXt_50, SE_ResNeXt_101
• SqueezeNet
• DenseNet
• ShuffleNetV2
• ResNet
• RegNet

For AlexNet and VGG, see :https://github.com/calmisential/TensorFlow2.0_Image_Classification
For InceptionV3, see :https://github.com/calmisential/TensorFlow2.0_InceptionV3
For ResNet, see :https://github.com/calmisential/TensorFlow2.0_ResNet

1. Requirements:

• Python >= 3.9
• Tensorflow >= 2.7.0
• tensorflow-addons >= 0.15.0

2. To train the network on your own dataset, you can put the dataset under the folderoriginal dataset, and the directory should look like this:

|——original dataset   |——class_name_0   |——class_name_1   |——class_name_2   |——class_name_3

3. Run the scriptsplit_dataset.py to split the raw dataset into train set, valid set and test set. The dataset directory will be like this:

|——dataset  |——train       |——class_name_1       |——class_name_2       ......       |——class_name_n  |——valid       |——class_name_1       |——class_name_2       ......       |——class_name_n  |—-test       |——class_name_1       |——class_name_2       ......       |——class_name_n

4. Runto_tfrecord.py to generate tfrecord files.
5. Change the corresponding parameters inconfig.py.
6. Runshow_model_list.py to get the index of model.
7. Runpython train.py --idx [index] to start training.
   If you want to train theEfficientNet, you should change the IMAGE_HEIGHT and IMAGE_WIDTH before training.

• b0 = (224, 224)
• b1 = (240, 240)
• b2 = (260, 260)
• b3 = (300, 300)
• b4 = (380, 380)
• b5 = (456, 456)
• b6 = (528, 528)
• b7 = (600, 600)

Runpython evaluate.py --idx [index] to evaluate the model's performance on the test dataset.

1. MobileNet_V1:Efficient Convolutional Neural Networks for Mobile Vision Applications
2. MobileNet_V2:Inverted Residuals and Linear Bottlenecks
3. MobileNet_V3:Searching for MobileNetV3
4. EfficientNet:EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
5. The official code of EfficientNet:https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
6. ResNeXt:Aggregated Residual Transformations for Deep Neural Networks
7. Inception_V4/Inception_ResNet_V1/Inception_ResNet_V2:Inception-v4, Inception-ResNet and the Impact of Residual Connectionson Learning
8. The official implementation of Inception_V4:https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py
9. The official implementation of Inception_ResNet_V2:https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py
10. SENet:Squeeze-and-Excitation Networks
11. SqueezeNet:SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
12. DenseNet:Densely Connected Convolutional Networks
13. https://zhuanlan.zhihu.com/p/37189203
14. ShuffleNetV2:ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
15. https://zhuanlan.zhihu.com/p/48261931
16. ResNet:Deep Residual Learning for Image Recognition
17. RegNet:Designing Network Design Spaces