| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: microsoft/swin-base-patch4-window12-384 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: swin-base-patch4-window12-384-finetuned-humid-classes-1 |
| | results: |
| | - task: |
| | name: Image Classification |
| | type: image-classification |
| | dataset: |
| | name: imagefolder |
| | type: imagefolder |
| | config: default |
| | split: validation |
| | args: default |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 1.0 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # swin-base-patch4-window12-384-finetuned-humid-classes-1 |
| |
|
| | This model is a fine-tuned version of [microsoft/swin-base-patch4-window12-384](https://huggingface.co/microsoft/swin-base-patch4-window12-384) on the imagefolder dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0096 |
| | - Accuracy: 1.0 |
| | - F1 Macro: 1.0 |
| | - Precision Macro: 1.0 |
| | - Recall Macro: 1.0 |
| | - Precision Dry: 1.0 |
| | - Recall Dry: 1.0 |
| | - F1 Dry: 1.0 |
| | - Precision Firm: 1.0 |
| | - Recall Firm: 1.0 |
| | - F1 Firm: 1.0 |
| | - Precision Humid: 1.0 |
| | - Recall Humid: 1.0 |
| | - F1 Humid: 1.0 |
| | - Precision Lump: 1.0 |
| | - Recall Lump: 1.0 |
| | - F1 Lump: 1.0 |
| | - Precision Rockies: 1.0 |
| | - Recall Rockies: 1.0 |
| | - F1 Rockies: 1.0 |
| |
|
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 32 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 50 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | Precision Dry | Recall Dry | F1 Dry | Precision Firm | Recall Firm | F1 Firm | Precision Humid | Recall Humid | F1 Humid | Precision Lump | Recall Lump | F1 Lump | Precision Rockies | Recall Rockies | F1 Rockies | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------------:|:------------:|:--------:|:--------------:|:-----------:|:-------:|:-----------------:|:--------------:|:----------:| |
| | | 1.4441 | 1.0 | 10 | 1.3328 | 0.5238 | 0.2903 | 0.2337 | 0.4 | 0.0 | 0.0 | 0.0 | 0.7368 | 1.0 | 0.8485 | 0.0 | 0.0 | 0.0 | 0.4318 | 1.0 | 0.6032 | 0.0 | 0.0 | 0.0 | |
| | | 1.0125 | 2.0 | 20 | 0.7622 | 0.6984 | 0.5484 | 0.6673 | 0.5922 | 1.0 | 0.8182 | 0.9 | 0.7778 | 1.0 | 0.875 | 0.0 | 0.0 | 0.0 | 0.5588 | 1.0 | 0.7170 | 1.0 | 0.1429 | 0.25 | |
| | | 0.4585 | 3.0 | 30 | 0.3032 | 0.8571 | 0.7864 | 0.9251 | 0.7723 | 1.0 | 1.0 | 1.0 | 0.9333 | 1.0 | 0.9655 | 1.0 | 0.2 | 0.3333 | 0.6923 | 0.9474 | 0.8 | 1.0 | 0.7143 | 0.8333 | |
| | | 0.2434 | 4.0 | 40 | 0.4579 | 0.8571 | 0.8227 | 0.8926 | 0.8131 | 1.0 | 1.0 | 1.0 | 0.8235 | 1.0 | 0.9032 | 1.0 | 0.4 | 0.5714 | 0.875 | 0.7368 | 0.8 | 0.7647 | 0.9286 | 0.8387 | |
| | | 0.171 | 5.0 | 50 | 0.1754 | 0.9365 | 0.9152 | 0.9523 | 0.8989 | 1.0 | 1.0 | 1.0 | 0.9333 | 1.0 | 0.9655 | 1.0 | 0.6 | 0.75 | 0.8947 | 0.8947 | 0.8947 | 0.9333 | 1.0 | 0.9655 | |
| | | 0.0951 | 6.0 | 60 | 0.1278 | 0.9683 | 0.9638 | 0.9533 | 0.9789 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8333 | 1.0 | 0.9091 | 1.0 | 0.8947 | 0.9444 | 0.9333 | 1.0 | 0.9655 | |
| | | 0.2476 | 7.0 | 70 | 0.1856 | 0.9365 | 0.8874 | 0.9581 | 0.8695 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.4 | 0.5714 | 0.8571 | 0.9474 | 0.9 | 0.9333 | 1.0 | 0.9655 | |
| | | 0.0706 | 8.0 | 80 | 0.2010 | 0.9206 | 0.8749 | 0.9433 | 0.8589 | 1.0 | 1.0 | 1.0 | 0.9333 | 1.0 | 0.9655 | 1.0 | 0.4 | 0.5714 | 0.85 | 0.8947 | 0.8718 | 0.9333 | 1.0 | 0.9655 | |
| | | 0.1555 | 9.0 | 90 | 0.5145 | 0.8413 | 0.8034 | 0.89 | 0.7875 | 1.0 | 1.0 | 1.0 | 0.7 | 1.0 | 0.8235 | 1.0 | 0.4 | 0.5714 | 0.85 | 0.8947 | 0.8718 | 0.9 | 0.6429 | 0.75 | |
| | | 0.0746 | 10.0 | 100 | 0.3848 | 0.9365 | 0.8874 | 0.9581 | 0.8695 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.4 | 0.5714 | 0.8571 | 0.9474 | 0.9 | 0.9333 | 1.0 | 0.9655 | |
| | | 0.2615 | 11.0 | 110 | 0.5946 | 0.8571 | 0.7836 | 0.9190 | 0.7723 | 1.0 | 1.0 | 1.0 | 0.875 | 1.0 | 0.9333 | 1.0 | 0.2 | 0.3333 | 0.72 | 0.9474 | 0.8182 | 1.0 | 0.7143 | 0.8333 | |
| | | 0.1522 | 12.0 | 120 | 0.2942 | 0.9206 | 0.8735 | 0.9464 | 0.8552 | 1.0 | 1.0 | 1.0 | 0.875 | 1.0 | 0.9333 | 1.0 | 0.4 | 0.5714 | 0.8571 | 0.9474 | 0.9 | 1.0 | 0.9286 | 0.9630 | |
| | | 0.0401 | 13.0 | 130 | 0.1079 | 0.9524 | 0.8997 | 0.9727 | 0.8800 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.4 | 0.5714 | 0.8636 | 1.0 | 0.9268 | 1.0 | 1.0 | 1.0 | |
| | | 0.0526 | 14.0 | 140 | 0.0778 | 0.9683 | 0.9495 | 0.9495 | 0.9495 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8 | 0.8 | 0.8 | 0.9474 | 0.9474 | 0.9474 | 1.0 | 1.0 | 1.0 | |
| | | 0.0078 | 15.0 | 150 | 0.0907 | 0.9524 | 0.9179 | 0.93 | 0.9095 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.75 | 0.6 | 0.6667 | 0.9 | 0.9474 | 0.9231 | 1.0 | 1.0 | 1.0 | |
| | | 0.0256 | 16.0 | 160 | 0.1176 | 0.9524 | 0.9509 | 0.9400 | 0.9684 | 1.0 | 1.0 | 1.0 | 0.9333 | 1.0 | 0.9655 | 0.8333 | 1.0 | 0.9091 | 1.0 | 0.8421 | 0.9143 | 0.9333 | 1.0 | 0.9655 | |
| | | 0.0168 | 17.0 | 170 | 0.1039 | 0.9524 | 0.9298 | 0.925 | 0.9571 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.625 | 1.0 | 0.7692 | 1.0 | 1.0 | 1.0 | 1.0 | 0.7857 | 0.88 | |
| | | 0.0004 | 18.0 | 180 | 0.1826 | 0.9524 | 0.8997 | 0.9727 | 0.8800 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.4 | 0.5714 | 0.8636 | 1.0 | 0.9268 | 1.0 | 1.0 | 1.0 | |
| | | 0.002 | 19.0 | 190 | 0.1104 | 0.9683 | 0.9539 | 0.9429 | 0.9752 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.7143 | 1.0 | 0.8333 | 1.0 | 0.9474 | 0.9730 | 1.0 | 0.9286 | 0.9630 | |
| | | 0.0176 | 20.0 | 200 | 1.0026 | 0.8889 | 0.8116 | 0.9462 | 0.7971 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.2 | 0.3333 | 0.7308 | 1.0 | 0.8444 | 1.0 | 0.7857 | 0.88 | |
| | | 0.0328 | 21.0 | 210 | 0.0096 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0001 | 22.0 | 220 | 0.1568 | 0.9524 | 0.8997 | 0.9727 | 0.8800 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.4 | 0.5714 | 0.8636 | 1.0 | 0.9268 | 1.0 | 1.0 | 1.0 | |
| | | 0.0005 | 23.0 | 230 | 0.0403 | 0.9683 | 0.9638 | 0.9533 | 0.9789 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8333 | 1.0 | 0.9091 | 1.0 | 0.8947 | 0.9444 | 0.9333 | 1.0 | 0.9655 | |
| | | 0.0002 | 24.0 | 240 | 0.0450 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0002 | 25.0 | 250 | 0.1338 | 0.9524 | 0.8997 | 0.9727 | 0.8800 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.4 | 0.5714 | 0.8636 | 1.0 | 0.9268 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 26.0 | 260 | 0.0212 | 0.9841 | 0.9764 | 0.9667 | 0.9895 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.8333 | 1.0 | 0.9091 | 1.0 | 0.9474 | 0.9730 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 27.0 | 270 | 0.0644 | 0.9683 | 0.9556 | 0.9429 | 0.9789 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.7143 | 1.0 | 0.8333 | 1.0 | 0.8947 | 0.9444 | 1.0 | 1.0 | 1.0 | |
| | | 0.0001 | 28.0 | 280 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 29.0 | 290 | 0.0342 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 30.0 | 300 | 0.0710 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 31.0 | 310 | 0.0844 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 32.0 | 320 | 0.0876 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 33.0 | 330 | 0.0894 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 34.0 | 340 | 0.0896 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 35.0 | 350 | 0.0823 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 36.0 | 360 | 0.0757 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 37.0 | 370 | 0.0721 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 38.0 | 380 | 0.0705 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 39.0 | 390 | 0.0669 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 40.0 | 400 | 0.0660 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0001 | 41.0 | 410 | 0.0691 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 42.0 | 420 | 0.0700 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 43.0 | 430 | 0.0711 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 44.0 | 440 | 0.0716 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0002 | 45.0 | 450 | 0.0804 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 46.0 | 460 | 0.0911 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 47.0 | 470 | 0.0935 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 48.0 | 480 | 0.0938 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0 | 49.0 | 490 | 0.0938 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | | 0.0041 | 50.0 | 500 | 0.0941 | 0.9683 | 0.9400 | 0.9810 | 0.9200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.6 | 0.75 | 0.9048 | 1.0 | 0.95 | 1.0 | 1.0 | 1.0 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.56.1 |
| | - Pytorch 2.9.0+cu126 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.22.0 |
| | |
