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https://github.com/alianse777/darknet-rust
A Rust wrapper for Darknet, an open source neural network framework written in C and CUDA.
https://github.com/alianse777/darknet-rust
Last synced: 12 days ago
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A Rust wrapper for Darknet, an open source neural network framework written in C and CUDA.
- Host: GitHub
- URL: https://github.com/alianse777/darknet-rust
- Owner: alianse777
- License: mit
- Created: 2020-03-18T06:14:01.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-05-02T06:04:36.000Z (about 1 year ago)
- Last Synced: 2024-03-14T21:06:39.802Z (3 months ago)
- Language: Rust
- Homepage: http://pjreddie.com/darknet/
- Size: 431 KB
- Stars: 31
- Watchers: 1
- Forks: 8
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-yolo-object-detection - alianse777/darknet-rust - rust?style=social"/> : A Rust wrapper for Darknet, an open source neural network framework written in C and CUDA. [pjreddie.com/darknet/](https://pjreddie.com/darknet/) (Other Versions of YOLO)
README
# darknet-rust: A Rust bindings for AlexeyAB's Darknet
[](https://crates.io/crates/darknet)
The crate is a Rust wrapper for [AlexeyAB's Darknet](https://github.com/AlexeyAB/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 0.4 changes:
- updated `image` to 0.24
## Examples
The **tiny_yolov3_inference** 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](https://docs.rs/darknet)
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.
Before running tests:
`git submodule init && git submodule update --recursive`
## Cargo Features
- `enable-cuda`: Enable CUDA (expects CUDA 10.x and cuDNN 7.x).
- `enable-cudnn`: Enable cuDNN
- `enable-opencv`: Enable OpenCV.
- `enable-openmp`: Enable OpenMP in darknet. Used for parallelization when running on the CPU. Enabled by default.
- `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 = "0.4"
```
You can optionally enable CUDA and OpenCV features. Please read [Build with CUDA](#build-with-cuda) for more info.
```toml[dependencies]
[dependencies]
darknet = {version = "0.4", 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 = "0.4", 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 = "0.4", 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 = "0.4", 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 all contributors!