⊞ Native YOLO-Master GUI Runner with ONNX/ncnn/MNN backends for Windows 10/11

⊞ For Windows 10/11: YOLO-Master Windows Runner (GUI) 1.0.0

An on-device YOLO-Master object detection and instance segmentation for Windows users, and accelerated via NVIDIA CUDA, Vulkan, or OpenCL. It infers on-device with CPU/GPU and nothing leaves the PC.

This is the first public release, and the companion to the  macOS Core ML Runner.

✨ Features

• Three Backends in One App: ONNX Runtime, ncnn, and MNN all ship in the same prebuilt binary. Switch backend from the sidebar and compare on the same image: the preprocessing and decode path is shared, so results match across all three.
• Detection & Segmentation: Runs both bounding-box detectors and instance-segmentation models (the same as the MacOS Core ML Runner).
• Images, Video & Live Camera: Single images, whole-folder batches, and MP4/AVI/MOV/MKV video, plus a low-latency live webcam mode with a real-time FPS / ms-per-frame HUD and a mirror toggle (the same as the MacOS Core ML Runner).
• Real-Time Tuning: Confidence, IoU (NMS), box style, and labels redraw instantly; the forward pass is cached, so tuning never re-runs inference. Letterbox vs. stretch preprocessing is also switchable (the same as the MacOS Core ML Runner).
• Two-Phase Pipeline: Folders and videos are inferred once with a progress bar, then browsed and scrubbed at full speed with the tuned parameters: a 30 fps clip plays back at 30 fps (the same as the MacOS Core ML Runner).
• CPU + GPU: One switch. ONNX runs on CUDA, ncnn on Vulkan, MNN on OpenCL, all in FP16 on the GPU; every backend falls back to CPU cleanly and disaplays the detailed error if a GPU backend is unavailable.
• Image Browser: Folder batches get a thumbnail grid or list view with a resizable icon size, and arrow-key navigation.

🚀 Performance

Live camera inference spoeed (higher = better) on NVIDIA RTX 5070Ti Laptop (Performance Mode), v0.1-seg-N at 640px:

Backend	Device	Infer	FPS
ONNX Runtime	CUDA	9.2ms	~108
MNN	OpenCL	19.7ms	50.8
ncnn	Vulkan	26.0ms	38.5
ONNX Runtime	CPU	41.6ms	24.0

**The runner can perform segmentation at real-time on a consumer laptop GPU. **

🖥️ Demo Screenshot

📥 Installation

Two builds are attached. Both are self-contained: unzip anywhere and run — no installer, no admin rights, no dependencies to download.

Pre-built Bundle	Size	Use Cases
YOLO-Master-Windows-1.0.0.zip	77.8 MB	You want the small download. GPU inference via ncnn-Vulkan / MNN-OpenCL.
YOLO-Master-Windows-CUDA-1.0.0.zip	1.20 GB	You want the fastest path: ONNX on CUDA. Bundles CUDA + cuDNN libraries.

1. Download a zip below and unzip it.
2. Run yolomaster_gui.exe.

The bundled segmentation model loads automatically, so you can open an image straight away.

On first launch, Windows SmartScreen may warn that the publisher is unrecognised: the executable is not code-signed like the Mac one. Click More info → Run anyway. Everything runs locally; the app makes no network requests.

In the CUDA build, the first inference after selecting Device → GPU can take 20 seconds while cuDNN selects convolution algorithms and the driver compiles kernels for your GPU. The window may look frozen; please be patient. Every run after that is super fast.

📦 Pre-exported Models

We ship the following pre-exported models:

model	nc	ONNX	MNN	ncnn
esmoe_n_visdrone	10	✅	✅	✅
esmoe_n_sku110k	1	✅	✅	✅
v0.1_n_visdrone	10	✅	✅	Modifying...
v0.1_n_sku110k	1	✅	✅	Modifying...
v0.1_n_aitodv2	8	✅	✅	Modifying...
v0.1_p2_n_aitodv2	8	✅	✅	Modifying...
uomoe_n_aitodv2	8	✅	✅	Modifying...
uomoe_p2_n_aitodv2	8	✅	✅	Modifying...

Why the six ncnn exports are missing? The v0.1 and UoMoE architectures gate their MoE experts with softmax → torch.topk → torch.gather. However, ncnn has no TopK or gather-by-index layer, so pnnx emits them as unregistered ops and load_param fails outright. Currently we are still attempting to implement TopK + Gather as custom ncnn layers in the runtime (ncnn::Net::register_custom_layer).

💻 Requirements

• Windows 10 (1703 or later) or Windows 11, 64-bit
• 4-core CPU and ≥4GB system memory recommended
• An NVIDIA GPU with a current driver for GPU inference. GeForce RTX 30xx series or newer recommended (Ampere / Ada Lovelace/ Blackwell). Vulkan and OpenCL come with the driver; the CUDA build additionally needs a CUDA 12-capable driver (R525+)
• No CUDA toolkit or cuDNN installation required.
• Runs on machines without a discrete GPU too, on CPU

🤝 Acknowledgements

Built as an extension edge tool of YOLO-Master.
We thank Ultralytics, ONNX Runtime, ncnn, MNN, OpenCV, and Dear ImGui for their great work.

Licensed under AGPL-3.0.