Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/armanx200/animal-detector
🐾 Training a machine learning model to recognize 15 different animal classes and classify images accordingly.
https://github.com/armanx200/animal-detector
animal-classification arman-kianian artificial-intelligence classification cnns computer-vision convolutional-neural-networks data-preprocessing data-science deep-learning github image-processing image-recognition keras machine-learning model-training neural-networks open-source python tensorflow
Last synced: 3 days ago
JSON representation
🐾 Training a machine learning model to recognize 15 different animal classes and classify images accordingly.
- Host: GitHub
- URL: https://github.com/armanx200/animal-detector
- Owner: Armanx200
- Created: 2024-05-31T08:51:24.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-09-30T20:51:27.000Z (4 months ago)
- Last Synced: 2024-11-24T09:16:34.793Z (2 months ago)
- Topics: animal-classification, arman-kianian, artificial-intelligence, classification, cnns, computer-vision, convolutional-neural-networks, data-preprocessing, data-science, deep-learning, github, image-processing, image-recognition, keras, machine-learning, model-training, neural-networks, open-source, python, tensorflow
- Language: Python
- Homepage: https://github.com/Armanx200
- Size: 35.1 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
```markdown
# 🐾 Animal Detector 🐾
Welcome to the Animal Detector project! This repository contains code and resources for training a machine learning model to recognize 15 different animal classes and classify images accordingly.
## 🦁 About the Project
This project uses a Convolutional Neural Network (CNN) to identify the following animals:
- Bear
- Bird
- Cat
- Cow
- Deer
- Dog
- Dolphin
- Elephant
- Giraffe
- Horse
- Kangaroo
- Lion
- Panda
- Tiger
- Zebra
The model is trained on images stored in the `animal_data` directory, and can classify new images provided by the user.
## 📁 Project Structure
Animal-Detector/
├── animal_data/
│ ├── Bear/
│ ├── Bird/
│ ├── Cat/
│ └── ... (other animal folders)
├── Animal-Detector.py
├── Animal-Detector-model.py
├── bear-1.jpg
├── README.md
└── requirements.txt
- `animal_data/`: Contains subdirectories for each animal class with training images.
- `Animal-Detector.py`: Script to classify a new image.
- `Animal-Detector-model.py`: Script to train and save the model.
- `bear-1.jpg`: Sample image for testing.
- `README.md`: Project documentation.
- `requirements.txt`: List of required Python packages.
## 🚀 Getting Started
### Prerequisites
Ensure you have Python installed along with the necessary packages:
```sh
pip install -r requirements.txt
```
### Training the Model
To train the model, run:
```sh
python Animal-Detector-model.py
```
This will train the CNN on the images in `animal_data/` and save the trained model as `animal_classifier_model.h5`.
### Classifying Images
To classify a new image, use:
```sh
python Animal-Detector.py path_to_your_image.jpg
```
Replace `path_to_your_image.jpg` with the path to the image you want to classify. The script will output the predicted animal class and confidence level.
## 🐍 Example Usage
Here's an example of how to use the classifier with the provided `bear-1.jpg` image:
```sh
python Animal-Detector.py bear-1.jpg
```
### Sample Output
```
This image is a Bear with 98.76% confidence.
```
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙌 Acknowledgements
- Inspired by the need to classify and detect animals using machine learning.
- Thanks to the TensorFlow and Keras communities for their excellent resources and support.
## 🤝 Contributing
Feel free to fork this repository and make improvements. Pull requests are welcome!
---
🔗 **Author**: [Armanx200](https://github.com/Armanx200)
```
This README includes emojis, a clear structure, and detailed instructions to make the project easy to understand and use.