https://github.com/hk669/yolov5
Object Detection Using YOLO with Self-Trained Data
https://github.com/hk669/yolov5
data-labeling labelimg object-detection training yolov5
Last synced: 7 months ago
JSON representation
Object Detection Using YOLO with Self-Trained Data
- Host: GitHub
- URL: https://github.com/hk669/yolov5
- Owner: Hk669
- Created: 2023-08-17T17:38:22.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-17T18:49:23.000Z (about 2 years ago)
- Last Synced: 2025-01-23T06:32:33.951Z (9 months ago)
- Topics: data-labeling, labelimg, object-detection, training, yolov5
- Language: Jupyter Notebook
- Homepage:
- Size: 2.42 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Object Detection Using YOLO with Self-Trained Data
This repository provides a guide on setting up and using YOLO (You Only Look Once) for object detection using self-trained data and a webcam. YOLO is a popular object detection algorithm that can efficiently detect objects in real-time.
## Prerequisites
- Python (>=3.6)
- YOLOv5: This guide uses YOLOv5 for object detection. Install it via:
```bash
pip install yolov5
```
## Getting Started
1. **Clone the Repository**:
Clone this repository to your local machine:
```bash
git clone https://github.com/Hk669/yolov5.git
cd cap-detection-yolo-webcam
```
2. **Prepare Your Custom Dataset**:
Collect and annotate images for your custom object detection task. You'll need to create annotations in YOLO format.
Use labelImg for labeling the images
3. **Training**:
Train the YOLO model on your custom dataset. You can follow the YOLOv5's official training guidelines.
4. **Export Model**:
After training, export the trained YOLO model.
5. **Webcam Object Detection**:
Run the webcam:
```bash
cap = cv2.VideoCapture(0)
for label in labels:
print('Collecting images for {}'.format(label))
time.sleep(5)
# loop through images
for img_num in range(number_images):
print('Collecting images for {}, image number {}'.format(label,img_num))
# webcam feed
ret, frame = cap.read()
# naming out image path
imgname = os.path.join(IMAGES_PATH, label+'.'+str(uuid.uuid1())+'.jpg')
# writes image to file
cv2.imwrite(imgname, frame)
# render to the screen
cv2.imshow('Image collection', frame)
time.sleep(2)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
## Project Structure
```
cap-detection-yolo-webcam/
│
├── data/ # Custom dataset and annotations
│ ├── images/
│ └── labels/
│
├── weights/ # Trained model weights
│
├── dataset.yml # Webcam object detection script
|
│
└── README.md
```
## Acknowledgments
- This project is built upon the YOLOv5 repository: [YOLOv5 GitHub](https://github.com/ultralytics/yolov5)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---