YOLO-Master-EsMoE-N Pre-exported Models

📦 YOLO-Master-EsMoE-N — VisDrone Models (v0.1)

Pre-exported YOLO-Master-EsMoE-N detection models, trained on VisDrone (10 classes), for use with the edge C++ runtime. All formats are the same graph; accuracy is validated on 548 VisDrone val images against the PyTorch original (mAP50-95 = 0.2036).

Assets

Asset	Format	Size	mAP50-95	Δ vs PyTorch	Notes
esmoe_n_visdrone_sim.onnx	ONNX (opset 12)	10.9 MB	0.2034	−0.02%	onnxsim-simplified, static 1×3×640×640
esmoe_n_visdrone_ncnn.zip	NCNN (pnnx)	9.4 MB	0.2034	−0.02%	unzip → folder with model.ncnn.param / .bin / metadata.yaml
esmoe_n_visdrone.mnn	MNN	10.8 MB	0.2034	−0.02%	converted from the ONNX via mnnconvert
esmoe_n_visdrone_int8_mixed.onnx	ONNX INT8	5.4 MB	0.1952	−0.84%	mixed-precision (head + attention + MoE-router kept FP32)

Class names, input size, and stride are embedded as model metadata — the runtime configures itself; no dataset YAML needed.

Usage

# ONNX (or INT8) — pass the file directly
yolomaster_edge --model esmoe_n_visdrone_sim.onnx --source path/to/image_or_dir --conf 0.25 --out out

# NCNN — unzip first, then point at the folder
unzip esmoe_n_visdrone_ncnn.zip
yolomaster_edge --model esmoe_n_visdrone_ncnn --source path/to/image_or_dir --conf 0.25 --out out

The backend is auto-detected from the model (.onnx → ONNX Runtime, ncnn folder → NCNN). See the repository's TECHNICAL_REPORT.md for the export pipeline, INT8 quantization methodology, and full parity/latency analysis.