https://github.com/hacker1649/fire-detection-yolov5
Implementing YOLOv5 for fire detection, blazing through images with precision. Harnessing deep learning to swiftly identify flames, safeguarding lives and properties.
https://github.com/hacker1649/fire-detection-yolov5
computer-vision deep-learning python3 ultralytics yolov5
Last synced: 4 months ago
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Implementing YOLOv5 for fire detection, blazing through images with precision. Harnessing deep learning to swiftly identify flames, safeguarding lives and properties.
- Host: GitHub
- URL: https://github.com/hacker1649/fire-detection-yolov5
- Owner: hacker1649
- License: mit
- Created: 2024-05-10T10:37:24.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-05-15T18:45:24.000Z (about 1 year ago)
- Last Synced: 2024-12-31T15:50:43.340Z (6 months ago)
- Topics: computer-vision, deep-learning, python3, ultralytics, yolov5
- Language: Jupyter Notebook
- Homepage:
- Size: 38.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Real Time Fire Detection Using YOLOv5
Fire detection task aims to identify fire or flame in an image or a video and put a bounding box around it. This repo includes a demo on how to build a fire detector using YOLOv5.
## 🛠️ Installation
1. Clone this repo
``` shell
# Clone
git clone https://github.com/hacker1649/fire-detection-YOLOv5.git
cd Fire-Detection-YOLOv5
```
2. Install [YOLOv5](https://github.com/ultralytics/yolov5).
``` shell
git clone https://github.com/ultralytics/yolov5.git
cd yolov5
pip install -r requirements.txt
```
## 🏋️ Training
I set up ```train.ipynb``` script for training the model from scratch. To train the model, download [Fire-Dataset](https://drive.google.com/file/d/1TQKA9nzo0BVwtmojmSusDt5j02KWzIu9/view?usp=sharing) and put it in ```datasets``` folder. This dataset contains samples from both [Fire & Smoke](https://www.kaggle.com/dataclusterlabs/fire-and-smoke-dataset) and [Fire & Guns](https://www.kaggle.com/atulyakumar98/fire-and-gun-dataset) datasets on Kaggle. I filtered out images and annotations that contain smokes & guns as well as images with low resolution, and then changed fire annotation's label in annotation files.
- YOLOv5
```
python train.py --img 640 --batch 16 --epochs 10 --data ../fire.yaml --weights yolov5s.pt --workers 0
```
## 🌱 Inference
- YOLOv5
If you train your own model, use the following command for detection:
``` shell
python detect.py --source ../input.mp4 --weights runs/train/exp/weights/best.pt --conf 0.2
```
Or you can use the pretrained model located in ```models``` folder for detection as follows:
``` shell
python detect.py --source ../input.mp4 --weights ../models/yolov5s_best.pt --conf 0.2
```