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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- image-classification |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-cat-emotions |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: custom dataset |
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type: imagefolder |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6352941176470588 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vit-base-cat-emotions |
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You can try out the model live [here](https://cat-emotion-classifier.streamlit.app/), and check out the [GitHub repository](https://github.com/semihdervis/cat-emotion-classifier) for more details. |
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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 custom dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0160 |
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- Accuracy: 0.6353 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.3361 | 3.125 | 100 | 1.0125 | 0.6548 | |
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| 0.0723 | 6.25 | 200 | 0.9043 | 0.7381 | |
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| 0.0321 | 9.375 | 300 | 0.9268 | 0.7143 | |
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### Framework versions |
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- Transformers 4.44.1 |
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- Pytorch 2.2.2+cu118 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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