Returns age group with about 91% accuracy based on facial image.
See https://www.kaggle.com/code/dima806/age-group-image-detection-vit for more details.
Classification report:
precision recall f1-score support
MIDDLE 0.8316 0.9278 0.8771 4321
YOUNG 0.9598 0.8563 0.9051 4322
OLD 0.9552 0.9477 0.9515 4322
accuracy 0.9106 12965
macro avg 0.9155 0.9106 0.9112 12965
weighted avg 0.9155 0.9106 0.9112 12965
checkpoint-13224/optimizer.pt 687 MBcheckpoint-4060/optimizer.pt 687 MBcheckpoint-4864/optimizer.pt 687 MBcheckpoint-912/optimizer.pt 687 MBcheckpoint-4060/pytorch_model.bin 343 MBpytorch_model.bin 343 MBcheckpoint-13224/model.safetensors 343 MBcheckpoint-4864/model.safetensors 343 MBcheckpoint-912/model.safetensors 343 MBmodel.safetensors 343 MBcheckpoint-13224/rng_state.pth 14.6 kBcheckpoint-4060/rng_state.pth 14.6 kBcheckpoint-4864/rng_state.pth 14.6 kBcheckpoint-912/rng_state.pth 14.6 kBcheckpoint-4864/training_args.bin 4.7 kBtraining_args.bin 4.7 kBcheckpoint-912/training_args.bin 4.4 kBcheckpoint-13224/training_args.bin 4.2 kBcheckpoint-4060/training_args.bin 4 kBcheckpoint-13224/scheduler.pt 0.6 kBcheckpoint-4060/scheduler.pt 0.6 kBcheckpoint-4864/scheduler.pt 0.6 kBcheckpoint-912/scheduler.pt 0.6 kB有文件？提交种子。
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