--- license: apache-2.0 base_model: facebook/deit-base-distilled-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: deit-base-distilled-patch16-224-hasta-75-fold4 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9166666666666666 --- # deit-base-distilled-patch16-224-hasta-75-fold4 This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4466 - Accuracy: 0.9167 ## 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 1.3783 | 0.0 | | No log | 2.0 | 2 | 1.1470 | 0.1667 | | No log | 3.0 | 3 | 0.7684 | 0.75 | | No log | 4.0 | 4 | 0.4466 | 0.9167 | | No log | 5.0 | 5 | 0.3116 | 0.9167 | | No log | 6.0 | 6 | 0.3024 | 0.9167 | | No log | 7.0 | 7 | 0.3113 | 0.9167 | | No log | 8.0 | 8 | 0.3526 | 0.9167 | | No log | 9.0 | 9 | 0.5370 | 0.9167 | | 0.3843 | 10.0 | 10 | 0.6259 | 0.8333 | | 0.3843 | 11.0 | 11 | 0.4979 | 0.9167 | | 0.3843 | 12.0 | 12 | 0.3555 | 0.9167 | | 0.3843 | 13.0 | 13 | 0.3132 | 0.9167 | | 0.3843 | 14.0 | 14 | 0.3054 | 0.9167 | | 0.3843 | 15.0 | 15 | 0.3262 | 0.9167 | | 0.3843 | 16.0 | 16 | 0.3495 | 0.9167 | | 0.3843 | 17.0 | 17 | 0.3211 | 0.9167 | | 0.3843 | 18.0 | 18 | 0.2993 | 0.9167 | | 0.3843 | 19.0 | 19 | 0.3174 | 0.9167 | | 0.1412 | 20.0 | 20 | 0.3221 | 0.8333 | | 0.1412 | 21.0 | 21 | 0.3248 | 0.8333 | | 0.1412 | 22.0 | 22 | 0.3245 | 0.8333 | | 0.1412 | 23.0 | 23 | 0.3412 | 0.8333 | | 0.1412 | 24.0 | 24 | 0.3021 | 0.8333 | | 0.1412 | 25.0 | 25 | 0.2038 | 0.9167 | | 0.1412 | 26.0 | 26 | 0.1856 | 0.9167 | | 0.1412 | 27.0 | 27 | 0.2126 | 0.9167 | | 0.1412 | 28.0 | 28 | 0.2161 | 0.9167 | | 0.1412 | 29.0 | 29 | 0.1838 | 0.9167 | | 0.0596 | 30.0 | 30 | 0.1688 | 0.9167 | | 0.0596 | 31.0 | 31 | 0.1827 | 0.9167 | | 0.0596 | 32.0 | 32 | 0.1860 | 0.9167 | | 0.0596 | 33.0 | 33 | 0.1819 | 0.9167 | | 0.0596 | 34.0 | 34 | 0.1868 | 0.9167 | | 0.0596 | 35.0 | 35 | 0.2212 | 0.8333 | | 0.0596 | 36.0 | 36 | 0.2478 | 0.8333 | | 0.0596 | 37.0 | 37 | 0.2653 | 0.8333 | | 0.0596 | 38.0 | 38 | 0.2093 | 0.9167 | | 0.0596 | 39.0 | 39 | 0.1924 | 0.9167 | | 0.0541 | 40.0 | 40 | 0.1789 | 0.9167 | | 0.0541 | 41.0 | 41 | 0.1646 | 0.9167 | | 0.0541 | 42.0 | 42 | 0.1635 | 0.9167 | | 0.0541 | 43.0 | 43 | 0.1611 | 0.9167 | | 0.0541 | 44.0 | 44 | 0.1592 | 0.9167 | | 0.0541 | 45.0 | 45 | 0.1754 | 0.9167 | | 0.0541 | 46.0 | 46 | 0.1908 | 0.9167 | | 0.0541 | 47.0 | 47 | 0.1859 | 0.9167 | | 0.0541 | 48.0 | 48 | 0.1687 | 0.9167 | | 0.0541 | 49.0 | 49 | 0.1646 | 0.9167 | | 0.0306 | 50.0 | 50 | 0.1663 | 0.9167 | | 0.0306 | 51.0 | 51 | 0.1609 | 0.9167 | | 0.0306 | 52.0 | 52 | 0.1791 | 0.9167 | | 0.0306 | 53.0 | 53 | 0.2029 | 0.9167 | | 0.0306 | 54.0 | 54 | 0.2205 | 0.9167 | | 0.0306 | 55.0 | 55 | 0.2358 | 0.9167 | | 0.0306 | 56.0 | 56 | 0.2392 | 0.9167 | | 0.0306 | 57.0 | 57 | 0.2591 | 0.9167 | | 0.0306 | 58.0 | 58 | 0.2536 | 0.9167 | | 0.0306 | 59.0 | 59 | 0.2678 | 0.9167 | | 0.0369 | 60.0 | 60 | 0.2655 | 0.9167 | | 0.0369 | 61.0 | 61 | 0.2782 | 0.9167 | | 0.0369 | 62.0 | 62 | 0.3050 | 0.9167 | | 0.0369 | 63.0 | 63 | 0.3199 | 0.9167 | | 0.0369 | 64.0 | 64 | 0.3130 | 0.9167 | | 0.0369 | 65.0 | 65 | 0.3063 | 0.9167 | | 0.0369 | 66.0 | 66 | 0.2885 | 0.9167 | | 0.0369 | 67.0 | 67 | 0.2654 | 0.9167 | | 0.0369 | 68.0 | 68 | 0.2478 | 0.9167 | | 0.0369 | 69.0 | 69 | 0.2358 | 0.9167 | | 0.0241 | 70.0 | 70 | 0.2106 | 0.9167 | | 0.0241 | 71.0 | 71 | 0.2047 | 0.9167 | | 0.0241 | 72.0 | 72 | 0.2100 | 0.9167 | | 0.0241 | 73.0 | 73 | 0.2092 | 0.9167 | | 0.0241 | 74.0 | 74 | 0.2261 | 0.9167 | | 0.0241 | 75.0 | 75 | 0.2380 | 0.9167 | | 0.0241 | 76.0 | 76 | 0.2644 | 0.9167 | | 0.0241 | 77.0 | 77 | 0.2972 | 0.9167 | | 0.0241 | 78.0 | 78 | 0.3053 | 0.9167 | | 0.0241 | 79.0 | 79 | 0.3133 | 0.9167 | | 0.0234 | 80.0 | 80 | 0.3051 | 0.9167 | | 0.0234 | 81.0 | 81 | 0.3001 | 0.9167 | | 0.0234 | 82.0 | 82 | 0.2921 | 0.9167 | | 0.0234 | 83.0 | 83 | 0.2899 | 0.9167 | | 0.0234 | 84.0 | 84 | 0.2798 | 0.9167 | | 0.0234 | 85.0 | 85 | 0.2641 | 0.9167 | | 0.0234 | 86.0 | 86 | 0.2514 | 0.9167 | | 0.0234 | 87.0 | 87 | 0.2419 | 0.9167 | | 0.0234 | 88.0 | 88 | 0.2282 | 0.9167 | | 0.0234 | 89.0 | 89 | 0.2174 | 0.9167 | | 0.0197 | 90.0 | 90 | 0.2070 | 0.9167 | | 0.0197 | 91.0 | 91 | 0.2006 | 0.9167 | | 0.0197 | 92.0 | 92 | 0.1977 | 0.9167 | | 0.0197 | 93.0 | 93 | 0.1956 | 0.9167 | | 0.0197 | 94.0 | 94 | 0.1946 | 0.9167 | | 0.0197 | 95.0 | 95 | 0.1931 | 0.9167 | | 0.0197 | 96.0 | 96 | 0.1920 | 0.9167 | | 0.0197 | 97.0 | 97 | 0.1922 | 0.9167 | | 0.0197 | 98.0 | 98 | 0.1926 | 0.9167 | | 0.0197 | 99.0 | 99 | 0.1942 | 0.9167 | | 0.0295 | 100.0 | 100 | 0.1950 | 0.9167 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1