| --- |
| 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. |
