--- license: apache-2.0 base_model: facebook/deit-small-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_deit_small_rms_001_fold1 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.7662771285475793 --- # smids_3x_deit_small_rms_001_fold1 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0295 - Accuracy: 0.7663 ## 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: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.9037 | 1.0 | 226 | 1.1611 | 0.4224 | | 0.8436 | 2.0 | 452 | 0.8419 | 0.5442 | | 0.8202 | 3.0 | 678 | 0.8414 | 0.5359 | | 0.8734 | 4.0 | 904 | 0.8332 | 0.5326 | | 0.8282 | 5.0 | 1130 | 0.7907 | 0.6127 | | 0.8721 | 6.0 | 1356 | 0.8061 | 0.5559 | | 0.7744 | 7.0 | 1582 | 0.7612 | 0.6260 | | 0.7444 | 8.0 | 1808 | 0.8606 | 0.5492 | | 0.7266 | 9.0 | 2034 | 0.7492 | 0.6427 | | 0.7385 | 10.0 | 2260 | 0.7643 | 0.6344 | | 0.6851 | 11.0 | 2486 | 0.7983 | 0.5843 | | 0.6844 | 12.0 | 2712 | 0.7946 | 0.6561 | | 0.6727 | 13.0 | 2938 | 0.8087 | 0.6244 | | 0.6244 | 14.0 | 3164 | 0.6709 | 0.6912 | | 0.6712 | 15.0 | 3390 | 0.6742 | 0.7095 | | 0.6346 | 16.0 | 3616 | 0.6684 | 0.7162 | | 0.5408 | 17.0 | 3842 | 0.6615 | 0.7028 | | 0.63 | 18.0 | 4068 | 0.6480 | 0.7295 | | 0.6263 | 19.0 | 4294 | 0.7205 | 0.6611 | | 0.5327 | 20.0 | 4520 | 0.6519 | 0.7078 | | 0.6622 | 21.0 | 4746 | 0.6350 | 0.7179 | | 0.6299 | 22.0 | 4972 | 0.8817 | 0.6210 | | 0.6304 | 23.0 | 5198 | 0.6476 | 0.7362 | | 0.5526 | 24.0 | 5424 | 0.6677 | 0.7145 | | 0.6295 | 25.0 | 5650 | 0.6118 | 0.7546 | | 0.6308 | 26.0 | 5876 | 0.6212 | 0.7362 | | 0.5383 | 27.0 | 6102 | 0.7015 | 0.7179 | | 0.5618 | 28.0 | 6328 | 0.8218 | 0.6711 | | 0.4879 | 29.0 | 6554 | 0.7043 | 0.6928 | | 0.5827 | 30.0 | 6780 | 0.6552 | 0.7229 | | 0.5364 | 31.0 | 7006 | 0.6340 | 0.7379 | | 0.4905 | 32.0 | 7232 | 0.6047 | 0.7529 | | 0.4492 | 33.0 | 7458 | 0.7039 | 0.7028 | | 0.4914 | 34.0 | 7684 | 0.6660 | 0.7379 | | 0.3519 | 35.0 | 7910 | 0.6494 | 0.7479 | | 0.3791 | 36.0 | 8136 | 0.6497 | 0.7513 | | 0.4111 | 37.0 | 8362 | 0.6075 | 0.7646 | | 0.4433 | 38.0 | 8588 | 0.6728 | 0.7679 | | 0.3357 | 39.0 | 8814 | 0.6576 | 0.7529 | | 0.3901 | 40.0 | 9040 | 0.6972 | 0.7596 | | 0.4094 | 41.0 | 9266 | 0.6481 | 0.7696 | | 0.3576 | 42.0 | 9492 | 0.6871 | 0.7746 | | 0.335 | 43.0 | 9718 | 0.7307 | 0.7846 | | 0.2737 | 44.0 | 9944 | 0.7687 | 0.7746 | | 0.3485 | 45.0 | 10170 | 0.7785 | 0.7780 | | 0.278 | 46.0 | 10396 | 0.8580 | 0.7730 | | 0.2622 | 47.0 | 10622 | 0.8921 | 0.7713 | | 0.2496 | 48.0 | 10848 | 0.9544 | 0.7730 | | 0.1441 | 49.0 | 11074 | 0.9744 | 0.7730 | | 0.1894 | 50.0 | 11300 | 1.0295 | 0.7663 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2