| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224 |
| | tags: |
| | - image-classification |
| | - animals |
| | - vision-transformer |
| | - vit |
| | - transfer-learning |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: vit-90-animals |
| | results: |
| | - task: |
| | name: Image Classification |
| | type: image-classification |
| | dataset: |
| | name: iamsouravbanerjee/animal-image-dataset-90-different-animals |
| | type: imagefolder |
| | config: default |
| | split: train |
| | args: default |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9796296296296296 |
| | --- |
| | ___ |
| | # vit-90-animals |
| | ___ |
| |
|
| | ## Model description |
| | This model is a fine-tuned Vision Transformer version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the [animal image dataset](https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals) from kaggle - trained to classify images into 90 different animal species. It achieves high accuracy on unseen data and was trained using supervised learning. The model can be used for general-purpose image classification in the animal domain and serves as a comparison baseline for zero-shot classification models such as CLIP. |
| |
|
| | The model achieves the following results on the evaluation set: |
| | - Loss: 0.0840 |
| | - Accuracy: 0.9796 |
| |
|
| | ## Intended uses & limitations |
| | ### Intended uses |
| | - Animal image classification (educational, demo, prototyping) |
| | - Benchmarking against zero-shot classification models |
| | - Use in Gradio interfaces or image analysis tools |
| |
|
| | ### Limitations |
| | - The model is limited to the 90 animal classes it was trained on |
| | - It may not generalize well to image domains outside of its training distribution |
| | - Performance can degrade with poor image quality or occlusions |
| |
|
| | ## Training and evaluation data |
| | The model was trained on a dataset containing 5,400 animal images categorized into 90 distinct classes. The dataset was obtained from Kaggle and according to the creator originally sourced from Google Images. The training/validation/test split was 80/10/10, and the label distribution is relatively balanced across classes. |
| |
|
| | Evaluation was conducted on the test split and compared to results from a zero-shot model (*openai/clip-vit-large-patch14*) using the same label set. |
| |
|
| | ## Training procedure |
| | - Base model: *google/vit-base-patch16-224* |
| | - Fine-tuning method: Supervised training using the Hugging Face Trainer class |
| | - Data augmentation: Applied during training (e.g., RandomHorizontalFlip, ColorJitter) |
| | - Training time: ~5 epochs with and without augmentation |
| | - Optimizer: AdamW (default settings) |
| | - Evaluation metrics: Accuracy, precision, and recall |
| | - Best performance (no augmentation): 98.3% test accuracy |
| |
|
| | ### Training hyperparameters |
| | The following hyperparameters were used during training: |
| | - learning_rate: 0.0003 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 5 |
| |
|
| | ### Training results |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | 1.2021 | 1.0 | 270 | 0.3500 | 0.9611 | |
| | | 0.2978 | 2.0 | 540 | 0.1766 | 0.9685 | |
| | | 0.1886 | 3.0 | 810 | 0.1500 | 0.9685 | |
| | | 0.1706 | 4.0 | 1080 | 0.1409 | 0.9685 | |
| | | 0.1678 | 5.0 | 1350 | 0.1373 | 0.9667 | |
| |
|
| | ### Framework versions |
| | - Transformers 4.50.0 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 3.4.1 |
| | - Tokenizers 0.21.1 |
| |
|
