Update README.md

README.md

@@ -7,29 +7,36 @@ sdk: gradio
 sdk_version: 6.12.0
 app_file: app.py
 pinned: false
-short_description: Compare ViT, CLIP, and OpenAI on 3 pet classes
+short_description: ViT vs CLIP vs OpenAI on 3 custom pet classes
 ---
 
 # Pet Classification Comparison
 
 This app compares 3 image classification approaches on pet images:
 
-- Fine-tuned ViT model (`vasanthi8134/oxford-pets-3class-vit`)
+- Fine-tuned ViT model ([vasanthi8134/oxford-pets-3class-vit](https://huggingface.co/vasanthi8134/oxford-pets-3class-vit))
 - Zero-shot CLIP model (`openai/clip-vit-base-patch32`)
 - OpenAI vision model (LLM image classification)
 
 ## Dataset Used For Training
 
 - Hugging Face dataset loader: `load_dataset("pcuenq/oxford-pets")`
-- Dataset type used in this project: **custom 3-class subset** based on Oxford-IIIT Pet dataset
-- Selected classes: `Egyptian Mau`, `leonberger`, `samoyed`
+- Original dataset source: Oxford-IIIT Pet dataset
+- Dataset used in this project: **custom 3-class subset** based on Oxford-IIIT Pet
+- Selected classes:
+  - `Egyptian Mau`
+  - `leonberger`
+  - `samoyed`
 - Number of classes: **3**
 - Total images: **90**
 
 ### Custom Split
-- Train: **60** images total (**20 per class**)
-- Validation: **15** images total (**5 per class**)
-- Test: **15** images total (**5 per class**)
+
+The custom subset was created by selecting **30 images per class** and splitting them into:
+
+- **Train:** 60 images total (**20 per class**)
+- **Validation:** 15 images total (**5 per class**)
+- **Test:** 15 images total (**5 per class**)
 
 ## Preprocessing Steps
 
@@ -39,7 +46,7 @@ This app compares 3 image classification approaches on pet images:
 - Conversion to tensor
 - Normalization with ViT image processor values
 
-### Validation/Test transforms
+### Validation / Test transforms
 - Resize
 - Center crop
 - Conversion to tensor
@@ -48,55 +55,90 @@ This app compares 3 image classification approaches on pet images:
 ## Trained Model
 
 - Base model: `google/vit-base-patch16-224-in21k`
-- Fine-tuned model link: `https://huggingface.co/vasanthi8134/oxford-pets-3class-vit`
+- Approach: **transfer learning / fine-tuning**
+- Fine-tuned model link: [https://huggingface.co/vasanthi8134/oxford-pets-3class-vit](https://huggingface.co/vasanthi8134/oxford-pets-3class-vit)
 
 ## Training Performance
 
-| Training Setup | Value |
+### Training Setup
+
+| Parameter | Value |
 |---|---:|
 | Epochs | 5 |
 | Learning rate | 5e-5 |
 | Batch size | 8 |
 
+### Final Evaluation
+
 | Metric | Value |
 |---|---:|
 | Validation accuracy | 1.0 |
 | Test accuracy | 1.0 |
 
+Because this project uses a small and simplified custom subset with only 3 classes, the fine-tuned model performs very well on this task.
+
+## Evaluation Method
+
+The final model was evaluated on:
+- a **validation split** during training
+- a separate **test split** after training
+
+The model with the best validation performance was used as the final selected model.
+
 ## Example Image Results
 
-The application compares:
-- my fine-tuned ViT model
-- CLIP zero-shot classification
-- OpenAI vision classification
+The table below reports example predictions from all 3 approaches.
+
+| Image | True Class | ViT Prediction | CLIP Prediction | OpenAI Prediction |
+|---|---|---|---|---|
+| `leonberger.jpg` | leonberger | leonberger (0.4457) | leonberger (1.0) | leonberger (0.95) |
+| `Egyptian_Mau.jpg` | Egyptian Mau | Egyptian Mau (0.4171) | Egyptian Mau (1.0) | Egyptian Mau (0.95) |
 
-### Example result: leonberger image
-| Model | Output |
-|---|---|
-| ViT | leonberger (0.4457) |
-| CLIP | leonberger (1.0) |
-| OpenAI | leonberger (0.95) |
+## Model Comparison
+
+This application compares:
+
+1. **My fine-tuned ViT model**
+2. **CLIP zero-shot classification**
+3. **OpenAI vision classification**
 
 ### Short comparison
-- **My model** is specialized for the selected 3 classes because it was fine-tuned on the custom subset.
-- **CLIP** works zero-shot and still performs well on clear images.
-- **OpenAI vision** also performs well and returns a label, confidence, and short reasoning.
 
-## Hugging Face App
+- **My fine-tuned ViT model** is specialized for the selected 3 classes because it was trained on the custom subset.
+- **CLIP** works in a zero-shot setting and still performs well on clear images without task-specific fine-tuning.
+- **OpenAI vision** also performs well and returns a label, confidence score, and short reasoning.
+
+## Hugging Face Links
 
-- App link: `https://huggingface.co/spaces/vasanthi8134/pet-classification-comparison`
+### Model
+[https://huggingface.co/vasanthi8134/oxford-pets-3class-vit](https://huggingface.co/vasanthi8134/oxford-pets-3class-vit)
+
+### App
+[https://huggingface.co/spaces/vasanthi8134/pet-classification-comparison](https://huggingface.co/spaces/vasanthi8134/pet-classification-comparison)
 
 ## Application Features
 
 The Hugging Face Space includes:
 - image upload
-- predictions from all three models
-- comparison output in JSON format
+- prediction from the fine-tuned ViT model
+- prediction from the zero-shot CLIP model
+- prediction from the OpenAI vision model
+- example images for quick testing
+- JSON output for direct comparison
+
+## Final Selected Model
+
+The final selected model for the custom classification task is:
+
+- **ViT fine-tuned on the custom 3-class Oxford-IIIT Pet subset**
+
+It was selected because it is the project-specific transfer learning model required by the assignment and achieved perfect accuracy on the simplified validation and test splits.
 
 ## Notes
 
-This is a simplified educational computer vision project created for model comparison using:
+This is a simplified educational computer vision project created to demonstrate:
+
 - transfer learning on custom data
 - Hugging Face model deployment
 - Hugging Face Space deployment
-- comparison between open-source and closed-source models
+- comparison between open-source and closed-source image classification approaches