mae-cnn/downstream_imagenet

Preparation for ImageNet-1k fine-tuning

See INSTALL.md to prepare dependencies and ImageNet dataset.

Training from pre-trained checkpoint

The script file for ImageNet-1k fine-tuning is downstream_imagenet/main.sh. Since torch.nn.parallel.DistributedDataParallel is used for distributed training, you are expected to specify some distributed arguments on each node, including:

• --num_nodes=<INTEGER>
• --ngpu_per_node=<INTEGER>
• --node_rank=<INTEGER>
• --master_address=<ADDRESS>
• --master_port=<INTEGER>

It is required to specify ImageNet data folder, model name, and checkpoint file path to run fine-tuning. All the other arguments have their default values, listed in downstream_imagenet/arg.py#L13. You can override any defaults by adding key-word arguments (like --bs=2048) to main.sh.

Here is an example command:

$ cd /path/to/SparK/downstream_imagenet
$ bash ./main.sh <experiment_name> \
--num_nodes=1 --ngpu_per_node=8 --node_rank=0 \
--master_address=128.0.0.0 --master_port=30000 \
--data_path=/path/to/imagenet \
--model=resnet50 --resume_from=/path/to/resnet50_1kpretrained.pth

Note that the first argument <experiment_name> is the name of your experiment, which would be used to create an output directory named output_<experiment_name>.