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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 |
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tags: |
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- image-classification |
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- chihiro |
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- studio-ghibli |
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- custom-dataset |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: chihiro-classifier-vit |
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results: |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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name: Custom Ghibli Dataset |
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type: imagefolder |
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metrics: |
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- name: Test Accuracy |
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type: accuracy |
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value: 0.9333 |
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- name: Zero-shot CLIP Accuracy |
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type: accuracy |
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value: 0.8667 |
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- name: Zero-shot Precision |
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type: precision |
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value: 0.8909 |
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- name: Zero-shot Recall |
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type: recall |
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value: 0.8667 |
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--- |
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<!-- This model card was customized based on training logs and evaluation metrics. --> |
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# chihiro-classifier-vit |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) trained on a small, custom binary classification dataset consisting of images labeled either "chihiro" or "not chihiro" (from Studio Ghibli films). |
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It was trained using PyTorch with transfer learning on a dataset of approximately 148 images. |
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## Model description |
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The model classifies images into one of two categories: **Chihiro** or **Not Chihiro**. It uses a Vision Transformer (ViT) backbone with a custom classification head for binary output. Data augmentation was used during training to improve generalization. Techniques included random horizontal flip, rotation (30°), color jitter, and random resized crop. |
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## Intended uses & limitations |
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**Intended Uses:** |
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- Student computer vision project |
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**Limitations:** |
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- Small dataset may limit real-world performance |
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- Not robust to domain shift or artistic variation |
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- Not intended for production deployment |
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## Training and evaluation data |
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- Custom image dataset of Chihiro vs. non-Chihiro characters |
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- Loaded using Hugging Face's `imagefolder` format |
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- Split: 80% train, 10% validation, 10% test |
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- Augmentation applied during training; deterministic preprocessing during eval |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam |
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- num_epochs: 12 |
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### Training results |
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| Epoch | Train Loss | Train Acc | Val Loss | Val Acc | |
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|:-----:|:----------:|:---------:|:--------:|:-------:| |
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| 1 | 0.8325 | 58.47% | 0.7285 | 46.67% | |
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| 2 | 0.6038 | 55.08% | 0.6931 | 60.00% | |
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| 3 | 0.6047 | 67.80% | 0.6170 | 66.67% | |
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| 4 | 0.4854 | 77.97% | 0.7272 | 66.67% | |
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| 5 | 0.3989 | 79.66% | 0.5494 | 66.67% | |
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| 6 | 0.3091 | 88.14% | 0.4649 | 86.67% | |
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| 7 | 0.2651 | 88.98% | 0.5736 | 73.33% | |
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| 8 | 0.2043 | 94.07% | 0.5335 | 73.33% | |
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| 9 | 0.2668 | 87.29% | 0.5765 | 80.00% | |
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| 10 | 0.2408 | 87.29% | 0.5346 | 73.33% | |
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| 11 | 0.1047 | 95.76% | 0.4125 | 73.33% | |
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| 12 | 0.1297 | 94.07% | 0.4084 | 86.67% | |
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### Final Test Evaluation |
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- `Test Loss`: 0.3677 |
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- `Test Accuracy`: 0.7333 |
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## 🧪 Zero-Shot CLIP Comparison |
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Evaluated using `openai/clip-vit-base-patch32` with no fine-tuning: |
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- `Zero-shot Accuracy`: 86.67% |
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- `Precision`: 0.8909 |
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- `Recall`: 0.8667 |
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## Framework versions |
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- Transformers: not used (custom PyTorch) |
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- PyTorch: 2.x |
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- Datasets: 2.x |
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- Tokenizers: N/A |
