File size: 18,805 Bytes
5470c55 4803a81 5470c55 4803a81 5470c55 4803a81 5470c55 4803a81 5470c55 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | ---
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
|