Comments (7)
Hi @tjasmin111! Thank you very much for your interest in our work!
The FireNet network was trained on a dataset with fire and smoke, but the MobileNet network was trained on a dataset with only fire images. However, the FireNet network is not very accurate for smoke detection. In that case, I suggest you use one of our approaches (YOLOv5 + AVT or YOLOv5 + TPT).
You can see more details in our paper:
-
Pedro Vinícius Almeida Borges de Venâncio, Roger Júnio Campos, Tamires Martins Rezende, Adriano Chaves Lisboa, Adriano Vilela Barbosa: A hybrid method for fire detection based on spatial and temporal patterns. In: Neural Computing and Applications, 2023.
from fire-detection.
Ok thank you for explaining this. But I don't knownwhy I have difficulty running your models. Baseline.py runs nicely but can't run detect.py at all
from fire-detection.
Did you install the dependencies in a Python environment? See the Dependencies section.
from fire-detection.
Yes. It would be best if you could have it as a docker image.
from fire-detection.
@tjasmin111 I just added a Docker image to the repository. Follow the steps in Tutorial to run the models.
If you like the work and want to help us spread it to the community, please star the repository. Thank you very much once again for your interest.
from fire-detection.
I tried it and the detections are pretty good. Was the model trained only on the D-fire dataset?
from fire-detection.
@tjasmin111 I'm glad the results were pretty good. Yes, the model was trained only with images from the D-Fire dataset.
from fire-detection.
Related Issues (5)
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from fire-detection.