| | --- |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224-in21k |
| | tags: |
| | - image-classification |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: id1 |
| | 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. --> |
| |
|
| | # id1 |
| |
|
| | 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 sooks/id1 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.6181 |
| | - Accuracy: 0.6535 |
| |
|
| | ## 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: 6 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:------:|:---------------:|:--------:| |
| | | 0.6933 | 0.53 | 10000 | 0.6932 | 0.5008 | |
| | | 0.6933 | 1.06 | 20000 | 0.6933 | 0.4992 | |
| | | 0.6933 | 1.59 | 30000 | 0.6931 | 0.5008 | |
| | | 0.6933 | 2.12 | 40000 | 0.6931 | 0.5161 | |
| | | 0.6931 | 2.65 | 50000 | 0.6933 | 0.4991 | |
| | | 0.6932 | 3.19 | 60000 | 0.6932 | 0.4991 | |
| | | 0.6746 | 3.72 | 70000 | 0.6725 | 0.5796 | |
| | | 0.6582 | 4.25 | 80000 | 0.6614 | 0.6032 | |
| | | 0.6455 | 4.78 | 90000 | 0.6466 | 0.6132 | |
| | | 0.6256 | 5.31 | 100000 | 0.6325 | 0.6391 | |
| | | 0.6144 | 5.84 | 110000 | 0.6181 | 0.6535 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.36.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.0 |
| |
|
