Benchmark task: Full-finetuning

Using a self-supervised model to initialize a network and further tune the weights on the target task is a very common evaluation protocol. This benchmark requires only initializing the model and no other settings in MODEL.FEATURE_EVAL_SETTINGS are needed unlike other benchmark tasks.

Benchmark: ImageNet-1k

VISSL provides the YAML configuration setting for this benchmark here which can be run as below.

python tools/run_distributed_engines.py \
  config=benchmark/imagenet1k_fulltune/eval_resnet_8gpu_transfer_in1k_fulltune \
  config.MODEL.WEIGHTS_INIT.PARAMS_FILE=<my_weights.torch>

Benchmark: Places205

VISSL provides the YAML configuration setting for this benchmark here which can be run as below.

python tools/run_distributed_engines.py \
  config=benchmark/places205_fulltune/eval_resnet_8gpu_transfer_places205_fulltune \
  config.MODEL.WEIGHTS_INIT.PARAMS_FILE=<my_weights.torch>

Note

Please see VISSL documentation on how to run a given training on 1-gpu, multi-gpu or multi-machine.

Note

Please see VISSL documentation on how to use the builtin datasets if you want to run this benchmark on a different target task..

Note

Please see VISSL documentation on how to use YAML comfiguration system in VISSL to override specific components like model of a config file. For example, in the above file, user can replace ResNet-50 model with a different architecture like RegNetY-256 etc. easily.