Welcome to Seeed Projects, a collection of demos and examples showcasing the capabilities of Seeed Studio products. This repository serves as a resource hub for developers, makers, and technology enthusiasts looking to explore and implement projects using Seeed Studio's innovative platforms.
Seeed Projects is an organized repository where you can find practical demonstrations and project ideas that leverage Seeed Studio's technology. Whether you are a beginner looking to get started with IoT devices, or an experienced developer seeking to expand your toolkit, this repository provides valuable resources to help you achieve your goals.
Each project in this repository is designed to provide a hands-on learning experience, demonstrating practical applications of Seeed Studio products. The projects range from simple tutorials to complex integrations, catering to a variety of skill levels and interests.
To get started with the projects in this repository, follow these steps:
- Explore the Projects: Browse through the repository to find projects that interest you.
- Read the Documentation: Each project comes with its own README file containing detailed instructions on setup, configuration, and operation.
- Download and Set Up: Clone the repository and follow the project-specific setup instructions.
- Contribute: If you have ideas for new projects or improvements, feel free to fork the repository and submit your contributions through pull requests.
We hope you find these projects inspiring and helpful in your journey with Seeed Studio's products. Happy building!
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| jetson-examples | The jetson-examples repository by Seeed Studio offers a seamless, one-line command deployment to run vision AI and Generative AI models on the NVIDIA Jetson platform. | 115 | Shell | 3 months ago |
| reComputer-Jetson-for-Beginners | Beginner's Guide to reComputer Jetson | 100 | Python | 3 months ago |
| RAG_based_on_Jetson | This project has implemented the RAG function on Jetson and supports TXT and PDF document formats. It uses MLC for 4-bit quantization of the Llama2-7b model, utilizes ChromaDB as the vector database, and connects these features with Llama_Index. I hope you like this project. | 7 | Python | 8 months ago |
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