vit-msn-small-beta-fia-individually-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: 0.7134
- Accuracy: 0.8028
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: 50
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.5714 | 1 | 1.8493 | 0.1268 |
| No log | 1.7143 | 3 | 1.7331 | 0.1268 |
| No log | 2.8571 | 5 | 1.4744 | 0.1268 |
| No log | 4.0 | 7 | 1.1333 | 0.2958 |
| No log | 4.5714 | 8 | 0.9675 | 0.6690 |
| 1.2028 | 5.7143 | 10 | 0.7486 | 0.8028 |
| 1.2028 | 6.8571 | 12 | 0.7134 | 0.8028 |
| 1.2028 | 8.0 | 14 | 0.7447 | 0.7958 |
| 1.2028 | 8.5714 | 15 | 0.7686 | 0.8028 |
| 1.2028 | 9.7143 | 17 | 0.7991 | 0.8028 |
| 1.2028 | 10.8571 | 19 | 0.7821 | 0.8028 |
| 0.7435 | 12.0 | 21 | 0.7589 | 0.8028 |
| 0.7435 | 12.5714 | 22 | 0.7497 | 0.8028 |
| 0.7435 | 13.7143 | 24 | 0.7412 | 0.8028 |
| 0.7435 | 14.8571 | 26 | 0.7626 | 0.8028 |
| 0.7435 | 16.0 | 28 | 0.7868 | 0.8028 |
| 0.7435 | 16.5714 | 29 | 0.7903 | 0.7958 |
| 0.6624 | 17.7143 | 31 | 0.7725 | 0.7958 |
| 0.6624 | 18.8571 | 33 | 0.7456 | 0.7887 |
| 0.6624 | 20.0 | 35 | 0.7403 | 0.8099 |
| 0.6624 | 20.5714 | 36 | 0.7444 | 0.8239 |
| 0.6624 | 21.7143 | 38 | 0.7515 | 0.8099 |
| 0.6099 | 22.8571 | 40 | 0.7579 | 0.8099 |
| 0.6099 | 24.0 | 42 | 0.7629 | 0.8099 |
| 0.6099 | 24.5714 | 43 | 0.7650 | 0.8169 |
| 0.6099 | 25.7143 | 45 | 0.7621 | 0.8169 |
| 0.6099 | 26.8571 | 47 | 0.7571 | 0.8099 |
| 0.6099 | 28.0 | 49 | 0.7551 | 0.8099 |
| 0.5882 | 28.5714 | 50 | 0.7551 | 0.8099 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1