Animal Detection

Inspiration

The inspiration comes from a curiosity about computer vision and its applications.

What it does

• The project takes an image as input and preprocesses the image.
• Utilize a machine learning model
• Once trained, the model can analyze the image to detect the presence of animals.
• For each detected object, the model assigns a label (e.g., 0 for Dog, 1 for Cat, 2 for Cow, etc.) based on the type of animal it recognizes.
• The output concisely summarizes the detected object, including its class and the model's confidence in the prediction. The visual representation allows for a more intuitive understanding of the detection results by displaying the identified object within the context of the original image.

How we built it

Using Keras, NumPy, Teachable Machine, Collab

What we learned

Building this project enhanced my understanding of computer vision fundamentals, data preprocessing, model training, annotation, evaluation metrics, model inference, visualization, real-world applications, and the iterative development process in object detection.

What's next for Object Detection in Images

Will work on UI.

Built With

• colab
• keras
• numpy