--- 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-patch16-224-65-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: 0.8169014084507042 --- # deit-base-distilled-patch16-224-65-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: 0.7218 - Accuracy: 0.8169 ## 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 | 0.9231 | 3 | 0.7266 | 0.4648 | | No log | 1.8462 | 6 | 0.8116 | 0.5211 | | No log | 2.7692 | 9 | 0.7081 | 0.4648 | | 0.7173 | 4.0 | 13 | 0.6645 | 0.5634 | | 0.7173 | 4.9231 | 16 | 0.6441 | 0.5915 | | 0.7173 | 5.8462 | 19 | 0.6400 | 0.6761 | | 0.6351 | 6.7692 | 22 | 0.6055 | 0.6620 | | 0.6351 | 8.0 | 26 | 0.7770 | 0.5352 | | 0.6351 | 8.9231 | 29 | 0.6259 | 0.6901 | | 0.5434 | 9.8462 | 32 | 0.5889 | 0.7183 | | 0.5434 | 10.7692 | 35 | 0.7283 | 0.6479 | | 0.5434 | 12.0 | 39 | 0.6898 | 0.6479 | | 0.4861 | 12.9231 | 42 | 0.6429 | 0.7183 | | 0.4861 | 13.8462 | 45 | 0.6915 | 0.6620 | | 0.4861 | 14.7692 | 48 | 0.5702 | 0.7183 | | 0.4285 | 16.0 | 52 | 0.6356 | 0.7042 | | 0.4285 | 16.9231 | 55 | 0.6981 | 0.6761 | | 0.4285 | 17.8462 | 58 | 0.5218 | 0.7183 | | 0.3781 | 18.7692 | 61 | 0.5340 | 0.7183 | | 0.3781 | 20.0 | 65 | 0.7611 | 0.6761 | | 0.3781 | 20.9231 | 68 | 0.5939 | 0.7465 | | 0.3516 | 21.8462 | 71 | 0.6186 | 0.7887 | | 0.3516 | 22.7692 | 74 | 0.7122 | 0.7042 | | 0.3516 | 24.0 | 78 | 0.5931 | 0.7887 | | 0.296 | 24.9231 | 81 | 0.6305 | 0.6901 | | 0.296 | 25.8462 | 84 | 0.8947 | 0.7042 | | 0.296 | 26.7692 | 87 | 0.6217 | 0.7183 | | 0.2741 | 28.0 | 91 | 0.7218 | 0.8169 | | 0.2741 | 28.9231 | 94 | 0.6687 | 0.7887 | | 0.2741 | 29.8462 | 97 | 0.6648 | 0.8028 | | 0.2559 | 30.7692 | 100 | 0.6433 | 0.7746 | | 0.2559 | 32.0 | 104 | 0.6674 | 0.7324 | | 0.2559 | 32.9231 | 107 | 0.6643 | 0.7465 | | 0.2001 | 33.8462 | 110 | 0.6247 | 0.7465 | | 0.2001 | 34.7692 | 113 | 0.6344 | 0.6901 | | 0.2001 | 36.0 | 117 | 0.7072 | 0.7606 | | 0.1728 | 36.9231 | 120 | 0.7146 | 0.7465 | | 0.1728 | 37.8462 | 123 | 0.8212 | 0.7606 | | 0.1728 | 38.7692 | 126 | 0.7901 | 0.7324 | | 0.2109 | 40.0 | 130 | 0.8235 | 0.7465 | | 0.2109 | 40.9231 | 133 | 0.9196 | 0.6901 | | 0.2109 | 41.8462 | 136 | 0.7758 | 0.7606 | | 0.2109 | 42.7692 | 139 | 0.7692 | 0.7183 | | 0.1634 | 44.0 | 143 | 0.8310 | 0.7606 | | 0.1634 | 44.9231 | 146 | 0.7550 | 0.7465 | | 0.1634 | 45.8462 | 149 | 0.7646 | 0.7324 | | 0.148 | 46.7692 | 152 | 0.7208 | 0.7606 | | 0.148 | 48.0 | 156 | 0.7324 | 0.7606 | | 0.148 | 48.9231 | 159 | 0.7856 | 0.7606 | | 0.1568 | 49.8462 | 162 | 0.8033 | 0.7606 | | 0.1568 | 50.7692 | 165 | 0.9007 | 0.7746 | | 0.1568 | 52.0 | 169 | 0.8179 | 0.7606 | | 0.1659 | 52.9231 | 172 | 0.7775 | 0.7606 | | 0.1659 | 53.8462 | 175 | 0.7214 | 0.7606 | | 0.1659 | 54.7692 | 178 | 0.7385 | 0.7465 | | 0.1352 | 56.0 | 182 | 0.7434 | 0.7465 | | 0.1352 | 56.9231 | 185 | 0.8971 | 0.7042 | | 0.1352 | 57.8462 | 188 | 0.7821 | 0.7606 | | 0.1309 | 58.7692 | 191 | 0.7896 | 0.7465 | | 0.1309 | 60.0 | 195 | 0.8340 | 0.7465 | | 0.1309 | 60.9231 | 198 | 0.8154 | 0.7746 | | 0.1201 | 61.8462 | 201 | 0.8185 | 0.7606 | | 0.1201 | 62.7692 | 204 | 0.9640 | 0.7183 | | 0.1201 | 64.0 | 208 | 0.8485 | 0.7606 | | 0.1291 | 64.9231 | 211 | 0.8807 | 0.7324 | | 0.1291 | 65.8462 | 214 | 0.8653 | 0.7183 | | 0.1291 | 66.7692 | 217 | 0.8744 | 0.7324 | | 0.124 | 68.0 | 221 | 0.8723 | 0.7324 | | 0.124 | 68.9231 | 224 | 0.8948 | 0.7606 | | 0.124 | 69.8462 | 227 | 0.9777 | 0.7183 | | 0.1262 | 70.7692 | 230 | 0.9409 | 0.7746 | | 0.1262 | 72.0 | 234 | 0.9618 | 0.7465 | | 0.1262 | 72.9231 | 237 | 0.9642 | 0.7606 | | 0.1036 | 73.8462 | 240 | 0.9738 | 0.7465 | | 0.1036 | 74.7692 | 243 | 0.9788 | 0.7324 | | 0.1036 | 76.0 | 247 | 1.0114 | 0.7465 | | 0.1183 | 76.9231 | 250 | 1.0004 | 0.7465 | | 0.1183 | 77.8462 | 253 | 1.0407 | 0.7465 | | 0.1183 | 78.7692 | 256 | 1.1510 | 0.7324 | | 0.0981 | 80.0 | 260 | 1.0718 | 0.7465 | | 0.0981 | 80.9231 | 263 | 0.9988 | 0.7324 | | 0.0981 | 81.8462 | 266 | 1.0054 | 0.7042 | | 0.0981 | 82.7692 | 269 | 0.9896 | 0.7324 | | 0.106 | 84.0 | 273 | 0.9851 | 0.7324 | | 0.106 | 84.9231 | 276 | 0.9770 | 0.7465 | | 0.106 | 85.8462 | 279 | 0.9623 | 0.7183 | | 0.114 | 86.7692 | 282 | 0.9664 | 0.7042 | | 0.114 | 88.0 | 286 | 0.9780 | 0.7042 | | 0.114 | 88.9231 | 289 | 0.9670 | 0.7183 | | 0.1157 | 89.8462 | 292 | 0.9586 | 0.7324 | | 0.1157 | 90.7692 | 295 | 0.9587 | 0.7183 | | 0.1157 | 92.0 | 299 | 0.9611 | 0.7042 | | 0.0834 | 92.3077 | 300 | 0.9612 | 0.7042 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1