Asl Detection Yolov5

🚀 Niki-stha/ASL Detection YOLOv5

This is a YOLOv5-based model for American Sign Language (ASL) detection, offering high precision in object detection tasks.

🚀 Quick Start

Installation

• Install yolov5:

pip install -U yolov5

Usage Examples

Basic Usage

import yolov5

# load model
model = yolov5.load('niki-stha/asl-detection-yolov5')
  
# set model parameters
model.conf = 0.80  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image

# set image
img = 'https://datasets-server.huggingface.co/assets/niki-stha/asl-detection-roboflow/--/niki-stha--asl-detection-roboflow/test/2/image/image.jpg'

# perform inference
results = model(img, size=640)

# inference with test time augmentation
results = model(img, augment=True)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

# show detection bounding boxes on image
results.show()

# save results into "results/" folder
results.save(save_dir='results/')

Advanced Usage

# Finetune the model on your custom dataset
yolov5 train --data data.yaml --img 416 --batch 32 --weights keremberke/yolov5s-license-plate --epochs 10

📚 Documentation

Model Information

Performance Metrics

On the validation split of the niki-stha/asl-detection-roboflow dataset, the model achieved a mAP@0.5 of 0.9854910682105946.

More Models

More models are available at: awesome-yolov5-models