VinayHajare
/

EfficientNetB0-finetuned-Marathi-Sign-Language

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 ---
 library_name: transformers
+tags:
+- Sign-Language
+- Marathi-Sign-Language
+license: mit
+datasets:
+- VinayHajare/Marathi-Sign-Language
+language:
+- mr
+metrics:
+- accuracy
+base_model:
+- google/efficientnet-b0
+pipeline_tag: image-classification
 ---
+# EfficientNetB0-finetuned-Marathi-Sign-Language
+A EfficientNetB0 finetune to identify Marathi-Sign-Language gesture and return its equivalent Devnagari Character.
 ## Model Details
 <!-- Provide a longer summary of what this model is. -->
+- **Developed by:** Vinay Arjun Hajare
+- **Model type:** Image-Classification
+- **Language(s) (NLP):** Marathi (mr)
+- **License:** MIT
+- **Finetuned from model:** google/efficientnet-b0
 ### Direct Use
+```python
+import gradio as gr
+from transformers import EfficientNetImageProcessor, EfficientNetForImageClassification
+from PIL import Image
+import torch
+# Load model and processor
+model_name = "VinayHajare/EfficientNetB0-finetuned-Marathi-Sign-Language"
+model = EfficientNetForImageClassification.from_pretrained(model_name)
+processor = EfficientNetImageProcessor.from_pretrained.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()
+```