--- library_name: transformers license: apache-2.0 base_model: microsoft/swinv2-tiny-patch4-window8-256 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swinv2-tiny-patch4-window8-256-dmae-humeda-DAV21 results: [] --- # swinv2-tiny-patch4-window8-256-dmae-humeda-DAV21 This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8631 - Accuracy: 0.7308 ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 42 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | No log | 0.9302 | 10 | 1.5510 | 0.3462 | | 6.5929 | 1.9302 | 20 | 1.4802 | 0.2692 | | 5.6252 | 2.9302 | 30 | 1.1115 | 0.4038 | | 3.874 | 3.9302 | 40 | 0.9996 | 0.5577 | | 2.7168 | 4.9302 | 50 | 0.8436 | 0.6538 | | 2.2435 | 5.9302 | 60 | 0.9320 | 0.6154 | | 2.2435 | 6.9302 | 70 | 0.8412 | 0.6346 | | 1.9334 | 7.9302 | 80 | 0.8622 | 0.6731 | | 1.6303 | 8.9302 | 90 | 0.9152 | 0.7115 | | 1.2748 | 9.9302 | 100 | 0.9721 | 0.6731 | | 1.0945 | 10.9302 | 110 | 1.0827 | 0.6538 | | 0.8395 | 11.9302 | 120 | 0.9153 | 0.7115 | | 0.8395 | 12.9302 | 130 | 0.8631 | 0.7308 | | 0.8587 | 13.9302 | 140 | 1.1039 | 0.6538 | | 0.8574 | 14.9302 | 150 | 1.0463 | 0.6923 | | 0.7096 | 15.9302 | 160 | 0.9991 | 0.7115 | | 0.6606 | 16.9302 | 170 | 1.0519 | 0.6731 | | 0.5513 | 17.9302 | 180 | 1.0865 | 0.7115 | | 0.5513 | 18.9302 | 190 | 1.1140 | 0.6731 | | 0.61 | 19.9302 | 200 | 1.0290 | 0.6731 | | 0.5278 | 20.9302 | 210 | 1.1003 | 0.6923 | | 0.4639 | 21.9302 | 220 | 1.2472 | 0.6538 | | 0.4719 | 22.9302 | 230 | 1.1546 | 0.6923 | | 0.4212 | 23.9302 | 240 | 1.1084 | 0.7308 | | 0.4212 | 24.9302 | 250 | 1.2953 | 0.6731 | | 0.4109 | 25.9302 | 260 | 1.1868 | 0.7308 | | 0.4236 | 26.9302 | 270 | 1.2560 | 0.6346 | | 0.3638 | 27.9302 | 280 | 1.2161 | 0.7115 | | 0.3944 | 28.9302 | 290 | 1.1582 | 0.7308 | | 0.3621 | 29.9302 | 300 | 1.2993 | 0.6923 | | 0.3621 | 30.9302 | 310 | 1.1401 | 0.7115 | | 0.3203 | 31.9302 | 320 | 1.3228 | 0.7115 | | 0.3014 | 32.9302 | 330 | 1.2813 | 0.6923 | | 0.3464 | 33.9302 | 340 | 1.4768 | 0.6538 | | 0.2891 | 34.9302 | 350 | 1.2304 | 0.7308 | | 0.3153 | 35.9302 | 360 | 1.3096 | 0.6923 | | 0.3153 | 36.9302 | 370 | 1.3565 | 0.7115 | | 0.2762 | 37.9302 | 380 | 1.2931 | 0.6923 | | 0.3191 | 38.9302 | 390 | 1.2441 | 0.7308 | | 0.3009 | 39.9302 | 400 | 1.2110 | 0.7308 | | 0.2645 | 40.9302 | 410 | 1.2433 | 0.7115 | | 0.2497 | 41.9302 | 420 | 1.2461 | 0.6923 | ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0