ResNet50 & ResNet101

https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz

The key difference of the full preactivation 'v2' variant compared to the 'v1' variant in [1] is the use of batch normalization before every weight layer.

• resnet(V1) ** ResNet V1 models use vgg pre-processing and input image size of 224*224 ** https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/vgg_preprocessing.py
• resnet(V2) according to the https://github.com/alexchungio/models/tree/master/research/slim ** ResNet V2 models use Inception pre-processing and input image size of 299*299 ** https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/inception_preprocessing.py

https://github.com/alexchungio/models/tree/master/research/slim

• batch size: 32
• learning rate: 0.01
• decay rate: 0.1
• num epoch percent decay: 20
• weight decay: 0.0001
• epoch: 60

• batch size: 32
• learning rate: 0.01
• decay rate: 0.1
• num epoch percent decay: 20
• weight decay: 0.0001
• epoch: 60