| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224 |
| tags: |
| - image-classification |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| model-index: |
| - name: fruits-classification |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: Schram03/fruits |
| type: imagefolder |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9280373831775701 |
| --- |
| |
| <!-- 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. --> |
|
|
| # fruits-classification |
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the augmented Schram03/fruits dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3039 |
| - Accuracy: 0.9280 |
|
|
| ## Model description |
|
|
| This model classifies the 33 different types of fruit in the Schram03/fruits dataset. |
|
|
| ## Intended uses & limitations |
|
|
| With further developement it could be possible to differentiat foul fruits or specific subtypes of fruit. This however would need an extensive Dataset. |
| Currently the model is able to differentiat the trained 33 types of fruit. However, there are limitations especially when using similar fruits like peaches and nectarines. |
|
|
| ## Training and evaluation data |
|
|
| 80 % Train, 20 % Evaluation |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0003 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.5816 | 1.0 | 1070 | 0.5238 | 0.8887 | |
| | 0.3761 | 2.0 | 2140 | 0.3568 | 0.9182 | |
| | 0.2934 | 3.0 | 3210 | 0.2852 | 0.9322 | |
| | 0.2719 | 4.0 | 4280 | 0.2681 | 0.9388 | |
| | 0.253 | 5.0 | 5350 | 0.2705 | 0.9388 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.52.3 |
| - Pytorch 2.7.0+cu128 |
| - Datasets 3.6.0 |
| - Tokenizers 0.21.1 |
|
|
