Similari VS tract

Hello, community. I have released a new version of Similari - 0.26.2.

Well, I never used the original DeepSORT implementation from the repo, but as I know you can pass any ReID to DeepSORT's predict if the metric is compatible. E.g. I have my own tracking library with DeepSORT flavor implemented. To run predict one just fill the structure like demonstrated here: https://github.com/insight-platform/Similari/blob/main/python/visual_sort.py

Benchmark results;

Both trackers support axis-aligned and rotated bounding boxes and work really fast (4-10 times faster than NumPy SORT depending on the number of objects in the scene).

Rust code (Similari framework repo): https://github.com/insight-platform/Similari/blob/main/src/utils/kalman.rs

My work is broadly connected with object trackers development, recently I have had a chance to publish an open source framework for developing object trackers. The framework name is Similari, it is written in Rust, making it a high-performance and robust solution. Feature vector operations are optimized for SIMD. I wrote an introductory article on Medium that explains how to build an IoU tracker with Similari: article. Also, the project repository contains a set of examples of how to construct trackers.

Several days ago, I posted the announcement about Similari - a rust framework for building tracker and similarity search engines. That post was positively accepted, but a couple of people wanted to learn more about the applicability of Similari. To fill the gap, I wrote an article on Medium where I demonstrated how to build a simple IoU tracker with Similari. I also do my best to fill the repo with useful samples - you can look at the samples directory to find how to build various trackers with the framework.

you can construct simple vectorwise searches, see an example, it's not as performant as HNSW but more universal;

Tract is the most well known ML crate in Rust, which I believe can compile to WASM - https://github.com/sonos/tract/. Burn may also be useful - https://github.com/burn-rs/burn.

tract!!

we use tract for inference, integrated into our runtime and services.

Not for training, but for inference this looks nice: https://github.com/sonos/tract

There's also tract, from sonos[0]. 100% rust.

I'm currently trying to use it to do speech recognition with a variant of the Conformer architecture (exported to ONNX).

The final goal is to do it in WASM client-side.

[0] https://github.com/sonos/tract

As the article notes, there isn't any official Rust-native support for any common frameworks.

tract (https://github.com/sonos/tract) seems like the most mature for ONNX (for which TF/PT export is good nowadays), and recently it successfully implemented BERT.

There are some DL libraries written in Rust: https://github.com/sonos/tract , https://docs.rs/neuronika/latest/neuronika/index.html . The second one could be used for training, I think.