UL base classification This model is a fine tuned version of google/vit base patch16 224 on the imagefolder dataset. It achieves the following results on the evaluation set: Loss: 0.3125 Accuracy: 0.8921 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The following hyperparameters were used during training: learning rate: 5e 05 train batch size: 32 eval batch size: 32 seed: 42 gradient accumulation steps: 4 total train batch size: 128 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e 08 lr scheduler type: linear lr scheduler warmup ratio: 0.1 num epochs: 7 Training results Training Loss Epoch Step Validation Loss Accuracy : : : : : : : : : : 0.8296 0.9756 20 0.5683 0.8230 0.4462 2.0 41 0.3949 0.8603 0.3588 2.9756 61 0.3633 0.8575 0.3196 4.0 82 0.3247 0.8852 0.2921 4.9756 102 0.3374 0.8728 0.2688 6.0 123 0.3125 0.8921 0.2366 6.8293 140 0.3137 0.8866 Framework versions Transformers 4.41.2 Pytorch 2.3.0+cu121 Datasets 2.19.2 Tokenizers 0.19.1
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