A tensorflow2 implementation of some basic CNNs(MobileNetV1/V2/V3, EfficientNet, ResNeXt, InceptionV4, InceptionResNetV1/V2, SENet, SqueezeNet, DenseNet, ShuffleNetV2, ResNet).
A tensorflow2 implementation of some basic CNNs.
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
|——original dataset
|——class_name_0
|——class_name_1
|——class_name_2
|——class_name_3
|——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
Run python evaluate.py --idx [index] to evaluate the model's performance on the test dataset.
Type | Neural Network | Input Image Size (height * width) |
---|---|---|
MobileNet | MobileNet_V1 | (224 * 224) |
MobileNet_V2 | (224 * 224) | |
MobileNet_V3 | (224 * 224) | |
EfficientNet | EfficientNet(B0~B7) | / |
ResNeXt | ResNeXt50 | (224 * 224) |
ResNeXt101 | (224 * 224) | |
SEResNeXt | SEResNeXt50 | (224 * 224) |
SEResNeXt101 | (224 * 224) | |
Inception | InceptionV4 | (299 * 299) |
Inception_ResNet_V1 | (299 * 299) | |
Inception_ResNet_V2 | (299 * 299) | |
SE_ResNet | SE_ResNet_50 | (224 * 224) |
SE_ResNet_101 | (224 * 224) | |
SE_ResNet_152 | (224 * 224) | SqueezeNet | SqueezeNet | (224 * 224) |
DenseNet | DenseNet_121 | (224 * 224) |
DenseNet_169 | (224 * 224) | |
DenseNet_201 | (224 * 224) | |
DenseNet_269 | (224 * 224) | |
ShuffleNetV2 | ShuffleNetV2 | (224 * 224) |
ResNet | ResNet_18 | (224 * 224) |
ResNet_34 | (224 * 224) | |
ResNet_50 | (224 * 224) | |
ResNet_101 | (224 * 224) | |
ResNet_152 | (224 * 224) |