--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: cards-blt-swin-tiny-patch4-window7-224-finetuned-v2 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: 0.5022222222222222 --- # cards-blt-swin-tiny-patch4-window7-224-finetuned-v2 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2162 - Accuracy: 0.5022 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4297 | 1.0 | 56 | 1.1976 | 0.4933 | | 1.4078 | 1.99 | 112 | 1.1964 | 0.5011 | | 1.417 | 2.99 | 168 | 1.2025 | 0.4961 | | 1.4163 | 4.0 | 225 | 1.2295 | 0.4883 | | 1.4318 | 5.0 | 281 | 1.2330 | 0.495 | | 1.4383 | 5.99 | 337 | 1.2162 | 0.5022 | | 1.4212 | 6.99 | 393 | 1.2634 | 0.4717 | | 1.4346 | 8.0 | 450 | 1.3083 | 0.4689 | | 1.419 | 9.0 | 506 | 1.2719 | 0.4806 | | 1.4252 | 9.99 | 562 | 1.3048 | 0.4911 | | 1.4522 | 10.99 | 618 | 1.2708 | 0.4794 | | 1.3748 | 12.0 | 675 | 1.3720 | 0.4383 | | 1.3966 | 13.0 | 731 | 1.3095 | 0.4594 | | 1.4507 | 13.99 | 787 | 1.2430 | 0.485 | | 1.4033 | 14.99 | 843 | 1.2728 | 0.4794 | | 1.3972 | 16.0 | 900 | 1.2611 | 0.4883 | | 1.4136 | 17.0 | 956 | 1.3166 | 0.45 | | 1.3992 | 17.99 | 1012 | 1.3103 | 0.4856 | | 1.3614 | 18.99 | 1068 | 1.3302 | 0.4422 | | 1.3747 | 20.0 | 1125 | 1.2919 | 0.4856 | | 1.3868 | 21.0 | 1181 | 1.3166 | 0.4728 | | 1.3399 | 21.99 | 1237 | 1.3200 | 0.4672 | | 1.3943 | 22.99 | 1293 | 1.2920 | 0.4811 | | 1.3635 | 24.0 | 1350 | 1.3109 | 0.4833 | | 1.3724 | 25.0 | 1406 | 1.3100 | 0.4644 | | 1.3141 | 25.99 | 1462 | 1.3263 | 0.4978 | | 1.3576 | 26.99 | 1518 | 1.3307 | 0.4772 | | 1.3022 | 28.0 | 1575 | 1.3409 | 0.4978 | | 1.2982 | 29.0 | 1631 | 1.3962 | 0.4583 | | 1.2657 | 29.99 | 1687 | 1.3329 | 0.4817 | | 1.3152 | 30.99 | 1743 | 1.2973 | 0.49 | | 1.2924 | 32.0 | 1800 | 1.3159 | 0.4833 | | 1.214 | 33.0 | 1856 | 1.3955 | 0.4833 | | 1.2717 | 33.99 | 1912 | 1.4583 | 0.46 | | 1.2692 | 34.99 | 1968 | 1.3504 | 0.4939 | | 1.2127 | 36.0 | 2025 | 1.3784 | 0.4833 | | 1.1956 | 37.0 | 2081 | 1.4184 | 0.4817 | | 1.2408 | 37.99 | 2137 | 1.3849 | 0.4944 | | 1.1699 | 38.99 | 2193 | 1.4298 | 0.4844 | | 1.1727 | 40.0 | 2250 | 1.4331 | 0.4772 | | 1.1485 | 41.0 | 2306 | 1.4597 | 0.4672 | | 1.1668 | 41.99 | 2362 | 1.4429 | 0.4783 | | 1.1881 | 42.99 | 2418 | 1.4555 | 0.4839 | | 1.1204 | 44.0 | 2475 | 1.4648 | 0.4783 | | 1.1523 | 45.0 | 2531 | 1.4744 | 0.4733 | | 1.1206 | 45.99 | 2587 | 1.4792 | 0.4906 | | 1.1135 | 46.99 | 2643 | 1.5009 | 0.4678 | | 1.1227 | 48.0 | 2700 | 1.5480 | 0.4733 | | 1.1017 | 49.0 | 2756 | 1.5907 | 0.4644 | | 1.1601 | 49.99 | 2812 | 1.5136 | 0.47 | | 1.1239 | 50.99 | 2868 | 1.5384 | 0.4789 | | 1.09 | 52.0 | 2925 | 1.5716 | 0.4711 | | 1.1023 | 53.0 | 2981 | 1.5736 | 0.4728 | | 1.1038 | 53.99 | 3037 | 1.5919 | 0.4556 | | 1.058 | 54.99 | 3093 | 1.5534 | 0.4772 | | 1.0405 | 56.0 | 3150 | 1.5788 | 0.4717 | | 1.0172 | 57.0 | 3206 | 1.5855 | 0.4767 | | 1.0036 | 57.99 | 3262 | 1.6425 | 0.455 | | 1.0124 | 58.99 | 3318 | 1.6039 | 0.4678 | | 1.0647 | 60.0 | 3375 | 1.5891 | 0.4572 | | 1.0143 | 61.0 | 3431 | 1.6265 | 0.4483 | | 1.0051 | 61.99 | 3487 | 1.6208 | 0.4633 | | 0.9571 | 62.99 | 3543 | 1.6874 | 0.4483 | | 0.9838 | 64.0 | 3600 | 1.6778 | 0.4517 | | 0.9995 | 65.0 | 3656 | 1.6248 | 0.4722 | | 1.0374 | 65.99 | 3712 | 1.6645 | 0.4667 | | 0.9483 | 66.99 | 3768 | 1.6307 | 0.4611 | | 0.9825 | 68.0 | 3825 | 1.6662 | 0.4661 | | 1.0023 | 69.0 | 3881 | 1.6650 | 0.46 | | 0.9642 | 69.99 | 3937 | 1.6953 | 0.4494 | | 0.9687 | 70.99 | 3993 | 1.7076 | 0.4661 | | 0.9542 | 72.0 | 4050 | 1.7012 | 0.4656 | | 0.9378 | 73.0 | 4106 | 1.7056 | 0.4533 | | 0.9542 | 73.99 | 4162 | 1.7331 | 0.4572 | | 0.9035 | 74.99 | 4218 | 1.7459 | 0.4417 | | 0.9631 | 76.0 | 4275 | 1.7236 | 0.465 | | 0.8759 | 77.0 | 4331 | 1.7294 | 0.455 | | 0.9218 | 77.99 | 4387 | 1.7654 | 0.4578 | | 0.9077 | 78.99 | 4443 | 1.7234 | 0.4594 | | 0.8924 | 80.0 | 4500 | 1.7256 | 0.4683 | | 0.9156 | 81.0 | 4556 | 1.7320 | 0.4678 | | 0.806 | 81.99 | 4612 | 1.7348 | 0.4661 | | 0.8863 | 82.99 | 4668 | 1.7514 | 0.4606 | | 0.8698 | 84.0 | 4725 | 1.7484 | 0.4661 | | 0.8623 | 85.0 | 4781 | 1.7420 | 0.4778 | | 0.8643 | 85.99 | 4837 | 1.7636 | 0.4617 | | 0.8914 | 86.99 | 4893 | 1.7552 | 0.465 | | 0.837 | 88.0 | 4950 | 1.7552 | 0.4644 | | 0.8217 | 89.0 | 5006 | 1.7532 | 0.4639 | | 0.8601 | 89.99 | 5062 | 1.7447 | 0.4683 | | 0.8293 | 90.99 | 5118 | 1.7622 | 0.4611 | | 0.8301 | 92.0 | 5175 | 1.7616 | 0.4633 | | 0.7752 | 93.0 | 5231 | 1.7585 | 0.4722 | | 0.8533 | 93.99 | 5287 | 1.7842 | 0.4617 | | 0.8156 | 94.99 | 5343 | 1.7837 | 0.4622 | | 0.8094 | 96.0 | 5400 | 1.7896 | 0.4583 | | 0.839 | 97.0 | 5456 | 1.7835 | 0.465 | | 0.839 | 97.99 | 5512 | 1.7883 | 0.46 | | 0.7763 | 98.99 | 5568 | 1.7838 | 0.4594 | | 0.8186 | 99.56 | 5600 | 1.7837 | 0.4606 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.15.2