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---
license: apache-2.0
datasets:
- Vedant3907/Hindi-Sign-Language-Dataset
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Hindi-Sign-Language-Detection
- SigLIP2
- 93M
---
# Hindi-Sign-Language-Detection
> Hindi-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 detect and classify Hindi sign language hand gestures into corresponding Devanagari characters using the SiglipForImageClassification architecture.
```py
Classification Report:
precision recall f1-score support
ऋ 0.9832 0.9121 0.9463 512
क 0.9433 0.9357 0.9395 498
ख 0.9694 0.9589 0.9641 462
ग 0.9961 0.8996 0.9454 568
घ 0.8990 0.9784 0.9370 464
ङ 0.9758 0.9869 0.9813 612
च 0.9223 0.9519 0.9368 561
छ 0.9226 0.9597 0.9408 571
ज 0.9346 0.9709 0.9524 412
झ 0.9051 0.9978 0.9492 449
ट 0.9670 0.8998 0.9322 489
ठ 0.8992 0.9954 0.9449 439
ढ 0.9392 0.9984 0.9679 634
ण 0.9102 0.9383 0.9240 648
त 0.8167 0.9938 0.8966 650
थ 0.9720 0.9616 0.9668 651
द 0.8162 0.9185 0.8643 319
न 0.9711 0.8971 0.9327 525
प 0.9642 0.9360 0.9499 719
फ 0.9847 0.7700 0.8642 500
ब 0.9447 0.9364 0.9406 566
भ 0.8779 0.9656 0.9197 581
म 0.9968 0.9920 0.9944 624
य 0.9600 0.9829 0.9713 586
र 0.9613 0.9268 0.9437 724
ल 0.9719 0.8993 0.9342 576
व 0.9619 0.8547 0.9052 709
स 1.0000 0.9721 0.9859 502
ह 0.9899 0.9441 0.9665 626
accuracy 0.9425 16177
macro avg 0.9433 0.9426 0.9413 16177
weighted avg 0.9457 0.9425 0.9425 16177
```
---
## Label Space: 29 Classes
The model classifies a hand sign into one of the following 29 Hindi characters:
```json
"id2label": {
"0": "ऋ",
"1": "क",
"2": "ख",
"3": "ग",
"4": "घ",
"5": "ङ",
"6": "च",
"7": "छ",
"8": "ज",
"9": "झ",
"10": "ट",
"11": "ठ",
"12": "ड",
"13": "ण",
"14": "त",
"15": "थ",
"16": "द",
"17": "न",
"18": "प",
"19": "फ",
"20": "ब",
"21": "भ",
"22": "म",
"23": "य",
"24": "र",
"25": "ल",
"26": "व",
"27": "स",
"28": "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/Hindi-Sign-Language-Detection" # Replace with actual path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Hindi label mapping
id2label = {
"0": "ऋ", "1": "क", "2": "ख", "3": "ग", "4": "घ",
"5": "ङ", "6": "च", "7": "छ", "8": "ज", "9": "झ",
"10": "ट", "11": "ठ", "12": "ड", "13": "ण", "14": "त",
"15": "थ", "16": "द", "17": "न", "18": "प", "19": "फ",
"20": "ब", "21": "भ", "22": "म", "23": "य", "24": "र",
"25": "ल", "26": "व", "27": "स", "28": "ह"
}
def classify_hindi_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_hindi_sign,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=3, label="Hindi Sign Classification"),
title="Hindi-Sign-Language-Detection",
description="Upload an image of a Hindi sign language hand gesture to identify the corresponding character."
)
if __name__ == "__main__":
iface.launch()
```
---
## Intended Use
Hindi-Sign-Language-Detection can be used in:
* Educational tools for learning Indian sign language.
* Assistive technology for hearing and speech-impaired individuals.
* Real-time sign-to-text translation applications.
* Human-computer interaction for Hindi users. |