This Streamlit app detects and classifies 60+ animal species using computer vision techniques and the YOLO object detection model.

• Real-time Detection: Detects and classifies a wide range of animal species in real-time using images or videos.
• Animal Classification: Animals are classified into categories such as mammals, birds, reptiles, amphibians, fish, and invertebrates.
• Upload Functionality: Users can upload images or videos (supported formats include jpg, jpeg, png, and mp4) for analysis.
• Display of Detected Animals: For each detected animal, the app shows the species and confidence score (accuracy of detection).
• User-friendly Interface: The app includes a sidebar that lists the possible animal classes for easy reference.

To run this app, you need to have Python installed. You can install the required packages using pip and the provided requirements.txt file:

This Streamlit app is designed for detecting and analyzing various animal species using computer vision techniques. It employs the YOLO (You Only Look Once) object detection model to identify different animal species in uploaded images or videos.

• Real-time species Detection: The app performs real-time detection of animal species such as mammals, birds, reptiles, amphibians, fish, and invertebrates.
• Species Classification: Detected species are categorized and displayed with their respective confidence scores (accuracy of detection).
• User Interface: The app features a user-friendly interface with sidebar navigation for different animal classes, making it easy to explore and analyze results.
• Upload Functionality: Users can upload images or videos (supported formats include jpg, jpeg, png, and mp4).

To run this app, you need to have Python installed. You can install the required packages using pip and the provided requirements.txt file:

pip install -r requirements.txt

1. Clone this repository:

git clone https://github.com/mehulnaik16/Animal_Species_detection_Streamlit.git
cd Animal_Species_detection_Streamlit

2. Install the required packages:

pip install -r requirements.txt

3. Run the Streamlit app:

streamlit run app.py

4. You can create a virtual environment (Better Option) or directly run code