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resnet

README.md

ResNet in TensorFlow

Deep residual networks, or ResNets for short, provided the breakthrough idea of identity mappings in order to enable training of very deep convolutional neural networks. This folder contains an implementation of ResNet for the ImageNet dataset written in TensorFlow.

See the following papers for more background:

[1] Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.

[2] Identity Mappings in Deep Residual Networks by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.

In code v1 refers to the resnet defined in [1], while v2 correspondingly refers to [2]. The principle difference between the two versions is that v1 applies batch normalization and activation after convolution, while v2 applies batch normalization, then activation, and finally convolution. A schematic comparison is presented in Figure 1 (left) of [2].

Please proceed according to which dataset you would like to train/evaluate on:

CIFAR-10

Setup

You simply need to have the latest version of TensorFlow installed. First make sure you've added the models folder to your Python path; otherwise you may encounter an error like ImportError: No module named official.resnet.

Then download and extract the CIFAR-10 data from Alex's website, specifying the location with the --data_dir flag. Run the following:

python cifar10_download_and_extract.py

Then to train the model, run the following:

python cifar10_main.py

Use --data_dir to specify the location of the CIFAR-10 data used in the previous step. There are more flag options as described in cifar10_main.py.

ImageNet

Setup

To begin, you will need to download the ImageNet dataset and convert it to TFRecord format. Follow along with the Inception guide in order to prepare the dataset.

Once your dataset is ready, you can begin training the model as follows:

python imagenet_main.py --data_dir=/path/to/imagenet

The model will begin training and will automatically evaluate itself on the validation data roughly once per epoch.

Note that there are a number of other options you can specify, including --model_dir to choose where to store the model and --resnet_size to choose the model size (options include ResNet-18 through ResNet-200). See resnet.py for the full list of options.

Compute Devices

Training is accomplished using the DistributionStrategies API. (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/distribute/README.md)

The appropriate distribution strategy is chosen based on the --num_gpus flag. By default this flag is one if TensorFlow is compiled with CUDA, and zero otherwise.

num_gpus:

  • 0: Use OneDeviceStrategy and train on CPU.
  • 1: Use OneDeviceStrategy and train on GPU.
  • 2+: Use MirroredStrategy (data parallelism) to distribute a batch between devices.

Pre-trained model

You can download 190 MB pre-trained versions of ResNet-50. Reported accuracies are top-1 single-crop accuracy for the ImageNet validation set. Simply download and uncompress the file, and point the model to the extracted directory using the --model_dir flag.

ResNet-50 v2 (Accuracy 76.05%):

ResNet-50 v2 (fp16, Accuracy 75.56%):

ResNet-50 v1 (Accuracy 75.91%):

Transfer Learning

You can use a pretrained model to initialize a training process. In addition you are able to freeze all but the final fully connected layers to fine tune your model. Transfer Learning is useful when training on your own small datasets. For a brief look at transfer learning in the context of convolutional neural networks, we recommend reading these short notes.

To fine tune a pretrained resnet you must make three changes to your training procedure:

  1. Build the exact same model as previously except we change the number of labels in the final classification layer.

  2. Restore all weights from the pre-trained resnet except for the final classification layer; this will get randomly initialized instead.

  3. Freeze earlier layers of the network

We can perform these three operations by specifying two flags: --pretrained_model_checkpoint_path and --fine_tune. The first flag is a string that points to the path of a pre-trained resnet model. If this flag is specified, it will load all but the final classification layer. A key thing to note: if both --pretrained_model_checkpoint_path and a non empty model_dir directory are passed, the tensorflow estimator will load only the model_dir. For more on this please see WarmStartSettings and Estimators.

The second flag --fine_tune is a boolean that indicates whether earlier layers of the network should be frozen. You may set this flag to false if you wish to continue training a pre-trained model from a checkpoint. If you set this flag to true, you can train a new classification layer from scratch.