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--- |
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library_name: transformers |
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tags: |
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- Sign-Language |
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- Marathi-Sign-Language |
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license: mit |
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datasets: |
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- VinayHajare/Marathi-Sign-Language |
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language: |
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- mr |
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metrics: |
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- accuracy |
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base_model: |
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- google/efficientnet-b0 |
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pipeline_tag: image-classification |
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--- |
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# EfficientNetB0-finetuned-Marathi-Sign-Language |
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A EfficientNetB0 finetune to identify Marathi-Sign-Language gesture and return its equivalent Devnagari Character. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Vinay Arjun Hajare |
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- **Model type:** Image-Classification |
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- **Language(s) (NLP):** Marathi (mr) |
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- **License:** MIT |
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- **Finetuned from model:** google/efficientnet-b0 |
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### Direct Use |
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```python |
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import gradio as gr |
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from transformers import EfficientNetImageProcessor, EfficientNetForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "VinayHajare/EfficientNetB0-finetuned-Marathi-Sign-Language" |
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model = EfficientNetForImageClassification.from_pretrained(model_name) |
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processor = EfficientNetImageProcessor.from_pretrained.from_pretrained(model_name) |
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# Marathi label mapping |
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id2label = { |
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"0": "अ", "1": "आ", "2": "इ", "3": "ई", "4": "उ", "5": "ऊ", |
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"6": "ए", "7": "ऐ", "8": "ओ", "9": "औ", "10": "क", "11": "क्ष", |
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"12": "ख", "13": "ग", "14": "घ", "15": "च", "16": "छ", "17": "ज", |
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"18": "ज्ञ", "19": "झ", "20": "ट", "21": "ठ", "22": "ड", "23": "ढ", |
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"24": "ण", "25": "त", "26": "थ", "27": "द", "28": "ध", "29": "न", |
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"30": "प", "31": "फ", "32": "ब", "33": "भ", "34": "म", "35": "य", |
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"36": "र", "37": "ल", "38": "ळ", "39": "व", "40": "श", "41": "स", "42": "ह" |
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} |
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def classify_marathi_sign(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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prediction = { |
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id2label[str(i)]: round(probs[i], 3) for i in range(len(probs)) |
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} |
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return prediction |
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# Gradio Interface |
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iface = gr.Interface( |
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fn=classify_marathi_sign, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=5, label="Marathi Sign Classification"), |
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title="Marathi-Sign-Language-Detection", |
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description="Upload an image of a Marathi sign language hand gesture to identify the corresponding character." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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