File size: 5,458 Bytes
11099bf 90a0b3c 11099bf 84e5004 11099bf 799f08e 11099bf 799f08e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- sign-language-detection
- alphabet
---
# **Alphabet-Sign-Language-Detection**
> **Alphabet-Sign-Language-Detection** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify images into **sign language alphabet** categories using the **SiglipForImageClassification** architecture.
```py
Classification Report:
precision recall f1-score support
A 0.9995 1.0000 0.9998 4384
B 1.0000 1.0000 1.0000 4441
C 1.0000 1.0000 1.0000 3993
D 1.0000 0.9998 0.9999 4940
E 1.0000 1.0000 1.0000 4658
F 1.0000 1.0000 1.0000 5750
G 0.9992 0.9996 0.9994 4978
H 1.0000 0.9979 0.9990 4807
I 0.9992 1.0000 0.9996 4856
J 1.0000 0.9996 0.9998 5227
K 0.9972 1.0000 0.9986 5426
L 1.0000 0.9998 0.9999 5089
M 1.0000 0.9964 0.9982 3328
N 0.9955 1.0000 0.9977 2635
O 0.9998 1.0000 0.9999 4564
P 1.0000 0.9993 0.9996 4100
Q 1.0000 1.0000 1.0000 4187
R 0.9998 0.9984 0.9991 5122
S 0.9998 0.9998 0.9998 5147
T 1.0000 1.0000 1.0000 4722
U 0.9984 0.9998 0.9991 5041
V 1.0000 0.9984 0.9992 5116
W 0.9998 1.0000 0.9999 4926
X 1.0000 0.9995 0.9998 4387
Y 1.0000 1.0000 1.0000 5185
Z 0.9996 1.0000 0.9998 4760
accuracy 0.9996 121769
macro avg 0.9995 0.9996 0.9995 121769
weighted avg 0.9996 0.9996 0.9996 121769
```
The model categorizes images into the following 26 classes:
- **Class 0:** "A"
- **Class 1:** "B"
- **Class 2:** "C"
- **Class 3:** "D"
- **Class 4:** "E"
- **Class 5:** "F"
- **Class 6:** "G"
- **Class 7:** "H"
- **Class 8:** "I"
- **Class 9:** "J"
- **Class 10:** "K"
- **Class 11:** "L"
- **Class 12:** "M"
- **Class 13:** "N"
- **Class 14:** "O"
- **Class 15:** "P"
- **Class 16:** "Q"
- **Class 17:** "R"
- **Class 18:** "S"
- **Class 19:** "T"
- **Class 20:** "U"
- **Class 21:** "V"
- **Class 22:** "W"
- **Class 23:** "X"
- **Class 24:** "Y"
- **Class 25:** "Z"
# **Run with Transformers🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Alphabet-Sign-Language-Detection"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def sign_language_classification(image):
"""Predicts sign language alphabet category for an 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()
labels = {
"0": "A", "1": "B", "2": "C", "3": "D", "4": "E", "5": "F", "6": "G", "7": "H", "8": "I", "9": "J",
"10": "K", "11": "L", "12": "M", "13": "N", "14": "O", "15": "P", "16": "Q", "17": "R", "18": "S", "19": "T",
"20": "U", "21": "V", "22": "W", "23": "X", "24": "Y", "25": "Z"
}
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=sign_language_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Alphabet Sign Language Detection",
description="Upload an image to classify it into one of the 26 sign language alphabet categories."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
# **Intended Use:**
The **Alphabet-Sign-Language-Detection** model is designed for sign language image classification. It helps categorize images of hand signs into predefined alphabet categories. Potential use cases include:
- **Sign Language Education:** Assisting learners in recognizing and practicing sign language alphabets.
- **Accessibility Enhancement:** Supporting applications that improve communication for the hearing impaired.
- **AI Research:** Advancing computer vision models in sign language recognition.
- **Gesture Recognition Systems:** Enabling interactive applications with real-time sign language detection. |