yolov8_onnx_julia
onnxruntime
| yolov8_onnx_julia | onnxruntime | |
|---|---|---|
| 1 | 55 | |
| 3 | 12,894 | |
| - | 3.9% | |
| 3.1 | 10.0 | |
| about 1 year ago | 2 days ago | |
| Julia | C++ | |
| GNU General Public License v3.0 only | MIT License |
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yolov8_onnx_julia
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How to create YOLOv8-based object detection web service using Python, Julia, Node.js, JavaScript, Go and Rust
You can find the source code of the Julia project in this repository.
onnxruntime
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Machine Learning with PHP
ONNX Runtime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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AI Inference now available in Supabase Edge Functions
Embedding generation uses the ONNX runtime under the hood. This is a cross-platform inferencing library that supports multiple execution providers from CPU to specialized GPUs.
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Deep Learning in JavaScript
tfjs is dead, looking at the commit history. The standard now is to convert PyTorch to onnx, then use onnxruntime (https://github.com/microsoft/onnxruntime/tree/main/js/web) to run the model on the browsdr.
- FLaNK Stack 05 Feb 2024
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Vcc – The Vulkan Clang Compiler
- slang[2] has the potential, but the meta programming part is not as strong as C++, existing libraries cannot be used.
The above conclusion is drawn from my work https://github.com/microsoft/onnxruntime/tree/dev/opencl, purely nightmare to work with thoes drivers and jit compilers. Hopefully Vcc can take compute shader more seriously.
[1]: https://www.circle-lang.org/
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Oracle-samples/sd4j: Stable Diffusion pipeline in Java using ONNX Runtime
I did. It depends what you want, for an overview of how ONNX Runtime works then Microsoft have a bunch of things on https://onnxruntime.ai, but the Java content is a bit lacking on there as I've not had time to write much. Eventually I'll probably write something similar to the C# SD tutorial they have on there but for the Java API.
For writing ONNX models from Java we added an ONNX export system to Tribuo in 2022 which can be used by anything on the JVM to export ONNX models in an easier way than writing a protobuf directly. Tribuo doesn't have full coverage of the ONNX spec, but we're happy to accept PRs to expand it, otherwise it'll fill out as we need it.
- Mamba-Chat: A Chat LLM based on State Space Models
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VectorDB: Vector Database Built by Kagi Search
What about models besides GPT? Most of the popular vector encoding models aren't using this architecture.
If you really didn't want PyTorch/Transformers, you could consider exporting your models to ONNX (https://github.com/microsoft/onnxruntime).
- ONNX runtime: Cross-platform accelerated machine learning
- Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
What are some alternatives?
tensor - package tensor provides efficient and generic n-dimensional arrays in Go that are useful for machine learning and deep learning purposes
onnx - Open standard for machine learning interoperability
yolov8_onnx_nodejs - YOLOv8 inference using Node.js
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
ort - A Rust wrapper for ONNX Runtime
onnx-simplifier - Simplify your onnx model
yolov8_onnx_rust - YOLOv8 inference using Rust
ONNX-YOLOv7-Object-Detection - Python scripts performing object detection using the YOLOv7 model in ONNX.
yolov8_pytorch_python - YOLOv8 inference using Ultralytics API
onnx-tensorflow - Tensorflow Backend for ONNX
ONNXRunTime.jl - Julia bindings for onnxruntime
MLflow - Open source platform for the machine learning lifecycle