| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: MultiLabel_V3 |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # MultiLabel_V3 |
| |
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.9683 |
| - Accuracy: 0.7370 |
| |
| ## 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: 0.0002 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 2 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.8572 | 0.1 | 100 | 1.1607 | 0.6466 | |
| | 0.8578 | 0.2 | 200 | 1.1956 | 0.6499 | |
| | 0.7362 | 0.3 | 300 | 1.1235 | 0.6885 | |
| | 0.8569 | 0.39 | 400 | 1.0460 | 0.6891 | |
| | 0.4851 | 0.49 | 500 | 1.1213 | 0.6891 | |
| | 0.7252 | 0.59 | 600 | 1.1512 | 0.6720 | |
| | 0.6333 | 0.69 | 700 | 1.1039 | 0.6913 | |
| | 0.6239 | 0.79 | 800 | 1.0636 | 0.7001 | |
| | 0.2768 | 0.89 | 900 | 1.0386 | 0.7073 | |
| | 0.4872 | 0.99 | 1000 | 1.0311 | 0.7062 | |
| | 0.3049 | 1.09 | 1100 | 1.0437 | 0.7155 | |
| | 0.1435 | 1.18 | 1200 | 1.0343 | 0.7222 | |
| | 0.2088 | 1.28 | 1300 | 1.0784 | 0.7194 | |
| | 0.4972 | 1.38 | 1400 | 1.1072 | 0.7166 | |
| | 0.3604 | 1.48 | 1500 | 1.0438 | 0.7150 | |
| | 0.2726 | 1.58 | 1600 | 1.0077 | 0.7293 | |
| | 0.3106 | 1.68 | 1700 | 1.0029 | 0.7326 | |
| | 0.3259 | 1.78 | 1800 | 0.9906 | 0.7310 | |
| | 0.3323 | 1.88 | 1900 | 0.9729 | 0.7359 | |
| | 0.2998 | 1.97 | 2000 | 0.9683 | 0.7370 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.26.1 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 2.10.1 |
| - Tokenizers 0.13.2 |
| |
