This project is a web-based application that allows users to upload an image and run object detection using a YOLOv5 model trained on the COCO2017 dataset. The app is built with Flask for the backend and uses YOLOv5 for detecting objects in the input images. Detected objects are highlighted with bounding boxes, and the processed image is displayed to the user.
- Upload an image via the web interface.
- Detects objects using YOLOv5 with COCO2017-trained weights.
- Displays the output image with bounding boxes for detected objects.
- Easy to deploy and use.
You can run the app locally on your PC. Just follow these steps:
- Clone the repository:
git clone https://github.com/PranitThombare/Object_detection_application.git cd Object_detection_application - Run the Command in Terminal:
python app.py
Make sure you have the following installed:
- Python 3.7+
- Flask
- PyTorch
- YOLOv5 (installed via PyTorch Hub)
- OpenCV
- PIL (Python Imaging Library)
Flask==2.1.1
torch==1.9.0
torchvision==0.10.0
opencv-python==4.5.3.56
Pillow==8.2.0