Update README.md

README.md

@@ -4,6 +4,14 @@ datasets:
 - frgfm/imagenette
 ---
 
+![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/XKcsM33R3XKl5JBBfQHNM.png)
+
+# IMAGENETTE
+
+> IMAGENETTE is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for multi-class image classification. It is trained to classify images into 10 categories from the popular Imagenette dataset using the SiglipForImageClassification architecture.
+
+
+
 ```py
 Classification Report:
                   precision    recall  f1-score   support
@@ -25,3 +33,97 @@ english springer     0.9843    0.9822    0.9832       955
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/74PN9tMCvZIfg_qegVOa9.png)
+
+---
+
+## Label Space: 10 Classes
+
+The model predicts one of the following image classes:
+
+```
+0: tench
+1: english springer
+2: cassette player
+3: chain saw
+4: church
+5: French horn
+6: garbage truck
+7: gas pump
+8: golf ball
+9: parachute
+```
+
+---
+
+## Install Dependencies
+
+```bash
+pip install -q transformers torch pillow gradio hf_xet
+```
+
+---
+
+## Inference Code
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/IMAGENETTE"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "tench",
+    "1": "english springer",
+    "2": "cassette player",
+    "3": "chain saw",
+    "4": "church",
+    "5": "French horn",
+    "6": "garbage truck",
+    "7": "gas pump",
+    "8": "golf ball",
+    "9": "parachute"
+}
+
+def classify_image(image):
+    image = Image.fromarray(image).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+    
+    prediction = {
+        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
+    }
+
+    return prediction
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=3, label="Image Classification"),
+    title="IMAGENETTE - SigLIP2 Classifier",
+    description="Upload an image to classify it into one of 10 categories from the Imagenette dataset."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## Intended Use
+
+IMAGENETTE is designed for:
+
+* Educational purposes and model benchmarking.
+* Demonstrating the performance of SigLIP2 on a small but diverse classification task.
+* Fine-tuning workflows on vision-language models.