@@ -51,4 +51,13 @@ The model will begin training and will automatically evaluate itself on the vali
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`](resnet.py) for the full list of options.
### Pre-trained model
You can download a 190 MB pre-trained version of ResNet-50 achieving 75.3% top-1 single-crop accuracy here: [resnet50_2017_11_30.tar.gz](http://download.tensorflow.org/models/official/resnet50_2017_11_30.tar.gz). Simply download and uncompress the file, and point the model to the extracted directory using the `--model_dir` flag.
You can download 190 MB pre-trained versions of ResNet-50 achieving 76.3% and 75.3% (respectively) top-1 single-crop accuracy here: [resnetv2_imagenet_checkpoint.tar.gz](http://download.tensorflow.org/models/official/resnetv2_imagenet_checkpoint.tar.gz), [resnetv1_imagenet_checkpoint.tar.gz](http://download.tensorflow.org/models/official/resnetv1_imagenet_checkpoint.tar.gz). Simply download and uncompress the file, and point the model to the extracted directory using the `--model_dir` flag.
