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| title: LN2 Computer Vision | |
| emoji: 💻 | |
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| sdk_version: 5.25.2 | |
| app_file: app.py | |
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| ## 🐶 Zero-Shot Classification on Oxford-IIIT Pet Dataset | |
| This model was evaluated on the **Oxford-IIIT Pet Dataset** in a zero-shot setting using Hugging Face's 🤗 `transformers` and `datasets` libraries. | |
| ### 🧪 Task: Zero-Shot Image Classification | |
| In this task, the model was used without any fine-tuning to classify pet images into breed categories. Each class label was passed as a candidate for classification using natural language descriptions. The model selected the most likely label for each image based on its learned knowledge. | |
| ### ✅ Results | |
| | Metric | Value | | |
| |------------|---------| | |
| | Accuracy | 88.00% | | |
| | Precision | 87.68% | | |
| | Recall | 88.00% | | |
| > ⚠️ Note: These results are based on zero-shot inference and do not reflect fine-tuned performance. | |
| ### 🧠 Model Used | |
| We used the pre-trained [`openai/clip-vit-base-patch32`](https://huggingface.co/openai/clip-vit-base-patch32) for zero-shot classification. | |