| license: apache-2.0 | |
| tags: | |
| - image-classification | |
| - generated_from_trainer | |
| datasets: | |
| - AI-Lab-Makerere/beans | |
| metrics: | |
| - accuracy | |
| widget: | |
| - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/healthy.jpeg | |
| example_title: Healthy | |
| - src: https://huggingface.co/jeraldflowers/vit_model/blob/main/bean_rust.jpeg | |
| example_title: Bean Rust | |
| model-index: | |
| - name: vit_model | |
| results: | |
| - task: | |
| type: image-classification | |
| name: Image Classification | |
| dataset: | |
| name: beans | |
| type: beans | |
| config: default | |
| split: train | |
| args: default | |
| metrics: | |
| - type: accuracy | |
| value: 1.0 | |
| name: Accuracy | |
| <!-- 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. --> | |
| # vit_model | |
| 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 beans dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0095 | |
| - Accuracy: 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: 0.0002 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.1526 | 3.85 | 500 | 0.0095 | 1.0 | | |
| ### Framework versions | |
| - Transformers 4.24.0 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.7.1 | |
| - Tokenizers 0.13.2 | |