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---
license: apache-2.0
tags:
- tensorflow-lite
- edge-ai
- asl-recognition
- mediapipe
- computer-vision
- gesture-recognition
library_name: tensorflow
inference: false
datasets: []
model-index:
- name: ASL-TFLite-Edge
results: []
---
# ASL-TFLite-Edge
This repository contains a TensorFlow Lite model trained to recognize American Sign Language (ASL) fingerspelling gestures using hand landmark data. The model is optimized for real-time inference on edge devices.
## 🧠 Model Details
- **Format:** TensorFlow Lite (.tflite)
- **Input:** 64x64 RGB image (generated from hand landmarks via Mediapipe)
- **Output:** Softmax probabilities over 59 ASL character classes (including a padding token)
- **Frameworks:** TensorFlow, Mediapipe
## 📁 Files Included
- `asl_model.tflite` – The TFLite model file for ASL recognition
- `inference_args.json` – JSON file containing the selected columns used for inference from parquet data
- `tflite_inference.py` – Inference script to run predictions from raw `.parquet` landmark files
## 🚀 How to Run Inference
You can download and load the TFLite model directly from Hugging Face using the `huggingface_hub` library.
### Clone the image
```bash
git lfs install
git clone https://huggingface.co/ColdSlim/ASL-TFLite-Edge
cd ASL-TFLite-Edge
```
### Requirements
```bash
pip install -r requirements.txt
```
### Run the Script
```bash
python tflite_inference.py path/to/sample.parquet
```
### Output
```bash
Predicted class index: 5
```
>🔁 You can map this class index back to the character using your `char_to_num` mapping used during training.
## 📌 Example Workflow
1. Extract right-hand landmark data from Mediapipe and store it in a `.parquet` file.
2. Ensure it contains the same selected_columns as in `inference_args.json`.
3. Run `tflite_inference.py` to get the predicted class.
## 🧾 License
This project is licensed under the Apache 2.0 License.
## 👨💻 Author
Developed by Manik Sheokand
For sign language fingerspelling Recognition on edge devices using TensorFlow Lite |