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Experiments on ImageNet

Although our method is designed for open-world recognition, we also provide the code for experiments on ImageNet for reference.

Create Image Loading List

Change the ImageNet dataset path in tools/create_loading_list.py and run:

cd imagenet/tools && python3 create_loading_list.py

Training

./scripts/run train.py configs/config_in1k.py

Evaluation

./scripts/run eval_hook.py evals/eval.py configs/config_in1k.py

Results

params training group R P F1
1.78B ImageNet-1K 0.896 0.432 0.574

The results are obtained by training the model on ImageNet-1K only and evaluating on the validation set.