This repository contains a Convolutional Neural Network (CNN) implementation for detecting temperatures in Flir images. The CNN model is trained to analyze thermal images and identify temperature patterns, making it useful for applications such as thermal anomaly detection and temperature mapping.
This repository houses a Convolutional Neural Network (CNN) model for temperature detection in Flir images. The CNN is trained to recognize temperature patterns and anomalies, providing a robust solution for applications in thermal imaging.
- CNN-based temperature detection algorithm
- Training pipeline for custom dataset
- Inference script for applying the model to new Flir images
- Results and accuracy metrics
- Python 3.x
- TensorFlow
- OpenCV
- [Add any other dependencies]
- Clone the repository:
git clone https://github.com/syedissambukhari/Flir-image-temperature-detection-using-CNN.git - Install dependencies:
pip install -r requirements.txt - [Add steps for training the model, preparing the dataset, and using the inference script]
The model is trained on a custom dataset containing Flir thermal images annotated with temperature labels. Due to the proprietary nature of Flir datasets, it is recommended to use your own dataset for training.
[Include information about model performance, accuracy, and any visualizations]
Contributions are welcome! Feel free to open issues or submit pull requests.