Update README.md

README.md

@@ -2,7 +2,20 @@
 license: apache-2.0
 datasets:
 - VinayHajare/Marathi-Sign-Language
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Marathi-Sign-Language-Detection
+- SigLIP2
+- 93M
 ---
+# Marathi-Sign-Language-Detection
+
+> Marathi-Sign-Language-Detection is a vision-language model fine-tuned from google/siglip2-base-patch16-224 for multi-class image classification. It is trained to recognize Marathi sign language hand gestures and map them to corresponding Devanagari characters using the SiglipForImageClassification architecture.
 
 ```py
 Classification Report:
@@ -55,4 +68,95 @@ Classification Report:
     accuracy                         0.9027     50099
    macro avg     0.9117    0.9039    0.9051     50099
 weighted avg     0.9107    0.9027    0.9040     50099
-```
+```
+
+---
+
+## Label Space: 43 Classes
+
+The model classifies a hand sign into one of the following 43 Marathi characters:
+
+```json
+"id2label": {
+  "0": "अ", "1": "आ", "2": "इ", "3": "ई", "4": "उ", "5": "ऊ",
+  "6": "ए", "7": "ऐ", "8": "ओ", "9": "औ", "10": "क", "11": "क्ष",
+  "12": "ख", "13": "ग", "14": "घ", "15": "च", "16": "छ", "17": "ज",
+  "18": "ज्ञ", "19": "झ", "20": "ट", "21": "ठ", "22": "ड", "23": "ढ",
+  "24": "ण", "25": "त", "26": "थ", "27": "द", "28": "ध", "29": "न",
+  "30": "प", "31": "फ", "32": "ब", "33": "भ", "34": "म", "35": "य",
+  "36": "र", "37": "ल", "38": "ळ", "39": "व", "40": "श", "41": "स", "42": "ह"
+}
+```
+
+---
+
+## Install Dependencies
+
+```bash
+pip install -q transformers torch pillow gradio
+```
+
+---
+
+## 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/Marathi-Sign-Language-Detection"  # Replace with actual path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Marathi label mapping
+id2label = {
+    "0": "अ", "1": "आ", "2": "इ", "3": "ई", "4": "उ", "5": "ऊ",
+    "6": "ए", "7": "ऐ", "8": "ओ", "9": "औ", "10": "क", "11": "क्ष",
+    "12": "ख", "13": "ग", "14": "घ", "15": "च", "16": "छ", "17": "ज",
+    "18": "ज्ञ", "19": "झ", "20": "ट", "21": "ठ", "22": "ड", "23": "ढ",
+    "24": "ण", "25": "त", "26": "थ", "27": "द", "28": "ध", "29": "न",
+    "30": "प", "31": "फ", "32": "ब", "33": "भ", "34": "म", "35": "य",
+    "36": "र", "37": "ल", "38": "ळ", "39": "व", "40": "श", "41": "स", "42": "ह"
+}
+
+def classify_marathi_sign(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_marathi_sign,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=5, label="Marathi Sign Classification"),
+    title="Marathi-Sign-Language-Detection",
+    description="Upload an image of a Marathi sign language hand gesture to identify the corresponding character."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## Intended Use
+
+Marathi-Sign-Language-Detection can be applied in:
+
+* Educational platforms for learning regional sign language.
+* Assistive communication tools for Marathi-speaking users with hearing impairments.
+* Interactive applications that translate signs into text.
+* Research and data collection for sign language development and recognition.