--- 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-patch16-224-75-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: 0.9302325581395349 --- # deit-base-distilled-patch16-224-75-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: 0.2564 - Accuracy: 0.9302 ## 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 | 2 | 0.5613 | 0.6744 | | No log | 2.0 | 4 | 0.6233 | 0.6977 | | No log | 3.0 | 6 | 0.7506 | 0.6977 | | No log | 4.0 | 8 | 0.6462 | 0.6977 | | 0.5231 | 5.0 | 10 | 0.3889 | 0.8140 | | 0.5231 | 6.0 | 12 | 0.3485 | 0.8372 | | 0.5231 | 7.0 | 14 | 0.4327 | 0.8372 | | 0.5231 | 8.0 | 16 | 0.4470 | 0.8372 | | 0.5231 | 9.0 | 18 | 0.3067 | 0.8605 | | 0.3699 | 10.0 | 20 | 0.3075 | 0.8605 | | 0.3699 | 11.0 | 22 | 0.5433 | 0.8372 | | 0.3699 | 12.0 | 24 | 0.5849 | 0.8140 | | 0.3699 | 13.0 | 26 | 0.4509 | 0.8372 | | 0.3699 | 14.0 | 28 | 0.5410 | 0.8372 | | 0.2951 | 15.0 | 30 | 0.4710 | 0.8140 | | 0.2951 | 16.0 | 32 | 0.3738 | 0.8605 | | 0.2951 | 17.0 | 34 | 0.3704 | 0.8372 | | 0.2951 | 18.0 | 36 | 0.2682 | 0.9070 | | 0.2951 | 19.0 | 38 | 0.4187 | 0.8372 | | 0.2279 | 20.0 | 40 | 0.3961 | 0.8372 | | 0.2279 | 21.0 | 42 | 0.2976 | 0.8837 | | 0.2279 | 22.0 | 44 | 0.5655 | 0.8140 | | 0.2279 | 23.0 | 46 | 0.4931 | 0.8372 | | 0.2279 | 24.0 | 48 | 0.2672 | 0.9070 | | 0.1823 | 25.0 | 50 | 0.2795 | 0.9070 | | 0.1823 | 26.0 | 52 | 0.3885 | 0.8605 | | 0.1823 | 27.0 | 54 | 0.2779 | 0.9070 | | 0.1823 | 28.0 | 56 | 0.2990 | 0.8605 | | 0.1823 | 29.0 | 58 | 0.3090 | 0.8605 | | 0.1314 | 30.0 | 60 | 0.2564 | 0.9302 | | 0.1314 | 31.0 | 62 | 0.2825 | 0.8605 | | 0.1314 | 32.0 | 64 | 0.3655 | 0.8605 | | 0.1314 | 33.0 | 66 | 0.2486 | 0.9302 | | 0.1314 | 34.0 | 68 | 0.2734 | 0.9070 | | 0.1387 | 35.0 | 70 | 0.5242 | 0.8372 | | 0.1387 | 36.0 | 72 | 0.5958 | 0.8372 | | 0.1387 | 37.0 | 74 | 0.3472 | 0.8837 | | 0.1387 | 38.0 | 76 | 0.3057 | 0.9070 | | 0.1387 | 39.0 | 78 | 0.4018 | 0.8372 | | 0.1266 | 40.0 | 80 | 0.3940 | 0.8372 | | 0.1266 | 41.0 | 82 | 0.3568 | 0.8605 | | 0.1266 | 42.0 | 84 | 0.3533 | 0.8837 | | 0.1266 | 43.0 | 86 | 0.3451 | 0.8837 | | 0.1266 | 44.0 | 88 | 0.3478 | 0.9070 | | 0.1004 | 45.0 | 90 | 0.3195 | 0.9302 | | 0.1004 | 46.0 | 92 | 0.3926 | 0.9070 | | 0.1004 | 47.0 | 94 | 0.4169 | 0.8837 | | 0.1004 | 48.0 | 96 | 0.4274 | 0.8837 | | 0.1004 | 49.0 | 98 | 0.4061 | 0.9070 | | 0.1033 | 50.0 | 100 | 0.4277 | 0.8605 | | 0.1033 | 51.0 | 102 | 0.3977 | 0.9070 | | 0.1033 | 52.0 | 104 | 0.4428 | 0.8605 | | 0.1033 | 53.0 | 106 | 0.6753 | 0.8140 | | 0.1033 | 54.0 | 108 | 0.6912 | 0.8140 | | 0.0827 | 55.0 | 110 | 0.4201 | 0.8605 | | 0.0827 | 56.0 | 112 | 0.3086 | 0.9302 | | 0.0827 | 57.0 | 114 | 0.3150 | 0.9302 | | 0.0827 | 58.0 | 116 | 0.4757 | 0.8605 | | 0.0827 | 59.0 | 118 | 0.6409 | 0.8605 | | 0.0889 | 60.0 | 120 | 0.5430 | 0.8837 | | 0.0889 | 61.0 | 122 | 0.4044 | 0.8837 | | 0.0889 | 62.0 | 124 | 0.3473 | 0.9302 | | 0.0889 | 63.0 | 126 | 0.3485 | 0.9302 | | 0.0889 | 64.0 | 128 | 0.3711 | 0.9302 | | 0.088 | 65.0 | 130 | 0.4405 | 0.8837 | | 0.088 | 66.0 | 132 | 0.6526 | 0.8605 | | 0.088 | 67.0 | 134 | 0.7019 | 0.8605 | | 0.088 | 68.0 | 136 | 0.5408 | 0.8605 | | 0.088 | 69.0 | 138 | 0.4057 | 0.9302 | | 0.0734 | 70.0 | 140 | 0.3797 | 0.9302 | | 0.0734 | 71.0 | 142 | 0.3811 | 0.9302 | | 0.0734 | 72.0 | 144 | 0.4040 | 0.9302 | | 0.0734 | 73.0 | 146 | 0.4567 | 0.8837 | | 0.0734 | 74.0 | 148 | 0.5161 | 0.9070 | | 0.0721 | 75.0 | 150 | 0.5240 | 0.8837 | | 0.0721 | 76.0 | 152 | 0.5048 | 0.9070 | | 0.0721 | 77.0 | 154 | 0.4635 | 0.9070 | | 0.0721 | 78.0 | 156 | 0.4510 | 0.9070 | | 0.0721 | 79.0 | 158 | 0.4931 | 0.9070 | | 0.0592 | 80.0 | 160 | 0.5368 | 0.8837 | | 0.0592 | 81.0 | 162 | 0.5297 | 0.8837 | | 0.0592 | 82.0 | 164 | 0.4722 | 0.9070 | | 0.0592 | 83.0 | 166 | 0.4179 | 0.9302 | | 0.0592 | 84.0 | 168 | 0.4045 | 0.9302 | | 0.0634 | 85.0 | 170 | 0.4200 | 0.9302 | | 0.0634 | 86.0 | 172 | 0.4497 | 0.9302 | | 0.0634 | 87.0 | 174 | 0.4796 | 0.9070 | | 0.0634 | 88.0 | 176 | 0.4997 | 0.8837 | | 0.0634 | 89.0 | 178 | 0.4867 | 0.8837 | | 0.0758 | 90.0 | 180 | 0.4478 | 0.9302 | | 0.0758 | 91.0 | 182 | 0.4145 | 0.9302 | | 0.0758 | 92.0 | 184 | 0.4036 | 0.9302 | | 0.0758 | 93.0 | 186 | 0.3877 | 0.9302 | | 0.0758 | 94.0 | 188 | 0.3747 | 0.9070 | | 0.0529 | 95.0 | 190 | 0.3685 | 0.9070 | | 0.0529 | 96.0 | 192 | 0.3644 | 0.9070 | | 0.0529 | 97.0 | 194 | 0.3648 | 0.9070 | | 0.0529 | 98.0 | 196 | 0.3663 | 0.9070 | | 0.0529 | 99.0 | 198 | 0.3686 | 0.9070 | | 0.0594 | 100.0 | 200 | 0.3693 | 0.9070 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1