darknet-rust: A Rust bindings for AlexeyAB's Darknet

The crate is a Rust wrapper for AlexeyAB's Darknet.

It provides the following features:

• Training and inference capabilities.
• Load config files and model weights from upstream without modifications.
• Safe type wrappers for C API. Includes network, detection and layer types.

Minimal rustc version: 1.43.0

Version 0.3.3 changes:

• traindetector now takes thresh and iouthresh arguments, version updated to reflect latest libdarknet repo.

Examples

The tinyyolov3inference example automatically downloads the YOLOv3 tiny weights, and produces inference results in output directory.

sh cargo run --release --example tiny_yolov3_inference

The run_inference example is an utility program that you can test a combination of model configs and weights on image files. For example, you can test the YOLOv4 mode.

sh cargo run --release --example run_inference -- \ --label-file darknet/data/coco.names \ --model-cfg darknet/cfg/yolov4.cfg \ --weights yolov4.weights \ darknet/data/*.jpg

Read the example code in examples/ to understand the actual usage. More model configs and weights can be found here: (https://pjreddie.com/darknet/yolo/).

Usage

API documentation

If you are using version 0.1, consider migrating to 0.3 or newer as several critical bugs and memory leakages were fixed.

Build

Terms used:

darknet-sys, darknet = Rust wrappers

libdarknet = C/C++ darknet implementation

By default, darknet will compile and link libdarknet statically. You can control the feature flags to change the behavior.

Cargo Features

• enable-cuda: Enable CUDA (expects CUDA 10.x and cuDNN 7.x).
• enable-cudnn: Enable cuDNN
• enable-opencv: Enable OpenCV.
• runtime: Link to libdarknet dynamic library. For example, libdark.so on Linux.
• dylib: Build dynamic library instead of static
• buildtime-bindgen: Generate bindings from libdarknet headers.

Method 1: Download and build from source (default)

toml [dependencies] darknet = "0.3.2" You can optionally enable CUDA and OpenCV features. Please read Build with CUDA for more info.

toml[dependencies] [dependencies] darknet = {version = "0.3.2", features = ["enable-cuda", "enable-opencv"] }

Method 2: Build with custom source

If you want to build with custom libdarknet source, point DARKNET_SRC environment variable to your source path. It should contain CMakeLists.txt.

sh export DARKNET_SRC=/path/to/your/darknet/repo

Method 3: Link to libdarknet dynamic library

With runtime feature, darknet-sys will not compile libdarknet source code and instead links to libdarknet dynamically. If you are using Linux, make sure libdark.so is installed on your system.

toml [dependencies] darknet = {version = "0.3.2", features = ["runtime"] }

Re-generate bindings

With buildtime-bindgen feature, darknet-sys re-generates bindings from headers. The option is necessary only when darkent is updated or modified.

toml [dependencies] darknet = {version = "0.3.2", features = ["buildtime-bindgen"] } If you want to use your (possibly modified) header files, point DARKNET_INCLUDE_PATH environment variable to your header dir.

Build with CUDA

Please check that both CUDA 10.x and cuDNN 7.x are installed.

Darknet reads CUDA_PATH environment variable (which defaults to /opt/cuda if not set) and assumes it can find cuda libraries at ${CUDA_PATH}/lib64.

sh export CUDA_PATH=/usr/local/cuda-10.1

toml [dependencies] darknet = {version = "0.3.2", features = ["enable-cuda", "enable-opencv"] } You can also set CUDA_ARCHITECTURES which is passed to libdarknet's cmake. It defaults to Auto, which auto-detects GPU architecture based on card present in the system during build.

License

The crate is licensed under MIT.

Credits

Huge thanks to jerry73204