I indexed 669 GB of my GoPro videos using my M1 Max computer and local ML models
A developer indexed over 600 GB of GoPro cycling videos using open-source ML models on an M1 Max, allowing for local search and direct export of clips to DaVinci Resolve.
Intelligence analysis by Gemini 2.5 Flash
Faced with thousands of GoPro videos from a cycling journey, the author created a local indexing solution leveraging an M1 Max and open-source ML models. This system enables efficient searching for specific moments within the footage and seamlessly integrates with DaVinci Resolve for editing.
Imagine you have a giant box full of all your favorite toy car videos, and you want to find that one specific moment where your car does a cool jump. Instead of watching all the videos, someone built a special computer program that watches them for you, remembers where all the cool jumps are, and even helps you put those parts together to make a new super video, all on your own super-fast computer, without sending your videos to the internet.
Analysis
The developer undertook a project to index 2,207 GoPro videos, totaling 668.68 GB and over 15 hours of footage, from a cycling journey. The primary goal was to efficiently locate specific, interesting moments within this large collection for editing purposes. The solution involved building a local indexing system on an M1 Max computer, utilizing open-source machine learning models.
Technical Implementation
The project's core relies on the M1 Max's processing power and its unified memory architecture, which allows all system RAM to be available as VRAM. This is a significant advantage for AI model workloads compared to conventional CPUs. The author integrated the indexing process with DaVinci Resolve, enabling direct export of identified video clips to the editing timeline.
Comparison with Existing Tools
While DaVinci Resolve 21 now includes a built-in AI IntelliSearch feature for video indexing, the author points out that custom-built solutions offer benefits like local processing, ensuring privacy, and the ability to integrate custom face data for enhanced labeling. Adobe Premiere Pro also offers similar features, but they are often cloud-processed, raising data privacy concerns for some users. Discussions in the comments also touched upon the potential for using Apple GPU with containers, with solutions like vllm-metal from Docker being suggested as a way to leverage MPS (Metal Performance Shaders) for improved GPU utilization in Docker environments.
Future Potential
The project demonstrates the power of local ML for personal data management, especially with the advancements in hardware like the M1 Max. There's potential for further development, such as embedding actual video clips for more nuanced action-related searches, beyond just frame-level embedding.
Key points
- Developer indexed 669 GB of GoPro videos (over 15 hours) using an M1 Max and local open-source ML models.
- The project allows searching for specific moments in videos and exporting clips directly to DaVinci Resolve.
- The M1 Max's unified memory and AI co-accelerator are key to handling these demanding ML workloads locally.
- Built-in video indexing exists in DaVinci Resolve 21, but the author's solution offers local processing and custom data integration.
- Discussions suggest ways to utilize Apple GPU with containers for improved ML performance.
This project showcases the growing potential for individuals to leverage powerful local hardware and open-source machine learning to manage personal data and media collections. It could inspire more developers to create privacy-focused, custom solutions for various media tasks, reducing reliance on cloud services and empowering users with greater control over their data.
Despite the impressive local capabilities, maintaining and updating custom ML models and tools can be challenging and time-consuming. Commercial software like DaVinci Resolve is rapidly integrating similar AI features, potentially making custom solutions less appealing for users who prefer out-of-the-box functionality over the complexities of DIY development.