加载中…
加载中…
checkpoint-32/optimizer.pt 687 MBcheckpoint-4688/optimizer.pt 687 MBcheckpoint-8752/optimizer.pt 687 MBcheckpoint-9376/optimizer.pt 687 MBcheckpoint-32/model.safetensors 343 MBcheckpoint-4688/model.safetensors 343 MBcheckpoint-8752/model.safetensors 343 MBcheckpoint-9376/model.safetensors 343 MBmodel.safetensors 343 MBcheckpoint-32/rng_state.pth 14.2 kBcheckpoint-4688/rng_state.pth 14.2 kBcheckpoint-8752/rng_state.pth 14.2 kBcheckpoint-9376/rng_state.pth 14.2 kBcheckpoint-32/training_args.bin 5.3 kBcheckpoint-4688/training_args.bin 5.3 kBcheckpoint-8752/training_args.bin 5.3 kBcheckpoint-9376/training_args.bin 5.3 kBtraining_args.bin 5.3 kBcheckpoint-32/scheduler.pt 1.1 kBcheckpoint-4688/scheduler.pt 1.1 kBcheckpoint-8752/scheduler.pt 1.1 kBcheckpoint-9376/scheduler.pt 1.1 kBDetects age group with about 59% accuracy based on an image.
See https://www.kaggle.com/code/dima806/age-group-image-classification-vit for details.
Classification report:
precision recall f1-score support
0-2 0.7803 0.7500 0.7649 180
3-9 0.7998 0.7998 0.7998 1249
10-19 0.5361 0.4236 0.4733 1086
20-29 0.6402 0.7221 0.6787 3026
30-39 0.4935 0.5083 0.5008 2099
40-49 0.4848 0.4386 0.4606 1238
50-59 0.5000 0.4814 0.4905 725
60-69 0.4497 0.4685 0.4589 286
more than 70 0.6897 0.1802 0.2857 111
accuracy 0.5892 10000
macro avg 0.5971 0.5303 0.5459 10000
weighted avg 0.5863 0.5892 0.5844 10000