Computer Vision Projects for Beginners

 📷 Computer Vision Projects for Beginners

1. Image Classification with CIFAR-10 or MNIST

What: Build a model to classify images into categories (e.g., handwritten digits or small object categories).

Why: It’s a classic beginner project that introduces you to CNNs (Convolutional Neural Networks).

Tools: Python, TensorFlow/Keras or PyTorch

Dataset: MNIST

, CIFAR-10

2. Face Detection with OpenCV

What: Detect faces in images or live video using pre-trained Haar cascades.

Why: Learn how to use OpenCV for image processing and real-time video capture.

Tools: Python, OpenCV

Extensions: Add smile detection or eye tracking.

3. Object Detection with YOLO or SSD

What: Detect and label objects in images or video.

Why: Introduces you to more advanced models that locate and classify multiple objects.

Tools: Python, OpenCV, pre-trained YOLO/SSD models

Dataset: Use pre-trained weights or try datasets like COCO.

4. Handwritten Digit Recognition Web App

What: Train a digit recognition model and deploy it as a simple web app.

Why: Practice model building and deployment.

Tools: Python, TensorFlow/Keras, Streamlit or Flask for the web app

Dataset: MNIST

5. Color Detection and Filtering

What: Identify specific colors in an image or video stream.

Why: Learn about image segmentation and color spaces (HSV, RGB).

Tools: Python, OpenCV

Extensions: Create a “virtual paint” app using color detection.

6. Image Filtering and Edge Detection

What: Apply filters like blur, sharpen, and edge detection (Sobel, Canny).

Why: Understand basic image processing techniques.

Tools: Python, OpenCV

7. Building a Cartoonifier

What: Convert photos into cartoon-style images using edge detection and color quantization.

Why: Fun project combining image processing and artistic effects.

Tools: Python, OpenCV

8. Traffic Sign Recognition

What: Classify images of traffic signs.

Why: Practical application of image classification.

Tools: TensorFlow/Keras or PyTorch

Dataset: German Traffic Sign Recognition Benchmark (GTSRB)

9. Pose Estimation with OpenPose or MediaPipe

What: Detect human poses and track body movements.

Why: Learn about keypoint detection and real-time video analysis.

Tools: Python, MediaPipe or OpenPose libraries

10. Simple OCR (Optical Character Recognition)

What: Extract text from images.

Why: Understand basic OCR techniques and libraries.

Tools: Python, Tesseract OCR

Tips for Success

Start with small projects and gradually add complexity.

Use pre-trained models when available to save time.

Document your projects on GitHub.

Share your work and get feedback in communities like Kaggle or GitHub.

Experiment with datasets from Kaggle, OpenML, or UCI ML Repository.

Learn AI ML Course in Hyderabad

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