vit-msn-small-beta-fia-equally-enhanced_test_1
This model is a fine-tuned version of facebook/vit-msn-small on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6061
- Accuracy: 0.8732
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.5714 | 1 | 1.5578 | 0.0704 |
| No log | 1.7143 | 3 | 1.4950 | 0.0634 |
| No log | 2.8571 | 5 | 1.3574 | 0.0634 |
| No log | 4.0 | 7 | 1.1698 | 0.1268 |
| No log | 4.5714 | 8 | 1.0682 | 0.3169 |
| 1.5036 | 5.7143 | 10 | 0.8754 | 0.7958 |
| 1.5036 | 6.8571 | 12 | 0.7359 | 0.8239 |
| 1.5036 | 8.0 | 14 | 0.6782 | 0.8169 |
| 1.5036 | 8.5714 | 15 | 0.6718 | 0.8169 |
| 1.5036 | 9.7143 | 17 | 0.6821 | 0.8099 |
| 1.5036 | 10.8571 | 19 | 0.7157 | 0.8028 |
| 0.7486 | 12.0 | 21 | 0.7173 | 0.8099 |
| 0.7486 | 12.5714 | 22 | 0.6967 | 0.8169 |
| 0.7486 | 13.7143 | 24 | 0.6847 | 0.8169 |
| 0.7486 | 14.8571 | 26 | 0.6827 | 0.8239 |
| 0.7486 | 16.0 | 28 | 0.6959 | 0.8380 |
| 0.7486 | 16.5714 | 29 | 0.6826 | 0.8521 |
| 0.6547 | 17.7143 | 31 | 0.6360 | 0.8310 |
| 0.6547 | 18.8571 | 33 | 0.6257 | 0.8521 |
| 0.6547 | 20.0 | 35 | 0.6594 | 0.8732 |
| 0.6547 | 20.5714 | 36 | 0.6784 | 0.8380 |
| 0.6547 | 21.7143 | 38 | 0.6578 | 0.8521 |
| 0.5817 | 22.8571 | 40 | 0.6146 | 0.8592 |
| 0.5817 | 24.0 | 42 | 0.6212 | 0.8732 |
| 0.5817 | 24.5714 | 43 | 0.6395 | 0.8732 |
| 0.5817 | 25.7143 | 45 | 0.6452 | 0.8732 |
| 0.5817 | 26.8571 | 47 | 0.6317 | 0.8803 |
| 0.5817 | 28.0 | 49 | 0.6332 | 0.8803 |
| 0.5632 | 28.5714 | 50 | 0.6418 | 0.8732 |
| 0.5632 | 29.7143 | 52 | 0.6383 | 0.8803 |
| 0.5632 | 30.8571 | 54 | 0.6367 | 0.8592 |
| 0.5632 | 32.0 | 56 | 0.6253 | 0.8732 |
| 0.5632 | 32.5714 | 57 | 0.6268 | 0.8592 |
| 0.5632 | 33.7143 | 59 | 0.6234 | 0.8662 |
| 0.5328 | 34.8571 | 61 | 0.6368 | 0.8521 |
| 0.5328 | 36.0 | 63 | 0.6251 | 0.8592 |
| 0.5328 | 36.5714 | 64 | 0.6184 | 0.8732 |
| 0.5328 | 37.7143 | 66 | 0.6067 | 0.8732 |
| 0.5328 | 38.8571 | 68 | 0.6182 | 0.8662 |
| 0.5272 | 40.0 | 70 | 0.6398 | 0.8451 |
| 0.5272 | 40.5714 | 71 | 0.6440 | 0.8310 |
| 0.5272 | 41.7143 | 73 | 0.6318 | 0.8451 |
| 0.5272 | 42.8571 | 75 | 0.6111 | 0.8732 |
| 0.5272 | 44.0 | 77 | 0.6061 | 0.8732 |
| 0.5272 | 44.5714 | 78 | 0.6116 | 0.8732 |
| 0.5255 | 45.7143 | 80 | 0.6320 | 0.8451 |
| 0.5255 | 46.8571 | 82 | 0.6394 | 0.8310 |
| 0.5255 | 48.0 | 84 | 0.6379 | 0.8310 |
| 0.5255 | 48.5714 | 85 | 0.6363 | 0.8310 |
| 0.5255 | 49.7143 | 87 | 0.6282 | 0.8521 |
| 0.5255 | 50.8571 | 89 | 0.6214 | 0.8592 |
| 0.52 | 52.0 | 91 | 0.6195 | 0.8592 |
| 0.52 | 52.5714 | 92 | 0.6170 | 0.8662 |
| 0.52 | 53.7143 | 94 | 0.6169 | 0.8592 |
| 0.52 | 54.8571 | 96 | 0.6174 | 0.8592 |
| 0.52 | 56.0 | 98 | 0.6187 | 0.8592 |
| 0.52 | 56.5714 | 99 | 0.6193 | 0.8592 |
| 0.504 | 57.1429 | 100 | 0.6194 | 0.8592 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1