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mudler/rfdetr-cpp-seg-nano

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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RF-DETR Seg-Nano — GGUF for rfdetr.cpp

GGUF-format weights of Roboflow RF-DETR Seg-Nano (segmentation variant) for use with rfdetr.cpp, a C++/ggml implementation that matches the upstream PyTorch model on CPU.

This repo contains all four standard quantizations of this variant. F16 is the recommended default — same accuracy as F32, 1.85× smaller, and typically the fastest on modern CPUs thanks to ggml's F32×F16 matmul fast path.

Available files

File	Quant	Size (MB)	Recall @ IoU 0.5	Recall @ IoU 0.95	Mean mask IoU	Pixel agreement	Latency (median ms, T=8)
`rfdetr-seg-nano-f32.gguf`	F32	127.1	0.9553	0.9553	0.9913	0.9998	114.7
`rfdetr-seg-nano-f16.gguf` ← recommended	F16	67.8	0.9267	0.9267	0.9911	0.9998	108.6
`rfdetr-seg-nano-q8_0.gguf`	Q8_0	39.9	0.9553	0.9553	0.9901	0.9998	119.0
`rfdetr-seg-nano-q4_K.gguf`	Q4_K	31.8	0.8949	0.6126	0.9636	0.9990	151.6

All accuracy numbers are computed against the upstream PyTorch reference (rfdetr 1.7.0) on 7 COCO val2017 images at threshold 0.5. Latency is measured with rfdetr-cli bench (8 iters + 3 warmup) at T=8 threads on a single AMD Ryzen 9 9950X3D image (coco_kitchen.jpg, 640x427).

Architecture

—Backbone: DINOv2-small
—Input resolution: 312×312
—Patch size: 12
—Decoder layers: 4
—Object queries: 100
—Task: instance segmentation (boxes + per-query masks)
—Mask resolution: 78×78 per query (image_size / 4)

Quantization notes

—F32 — full-precision reference, ~120 MB. Bit-exact PyTorch parity.
—F16 — matmul-multiplicand weights only; LayerNorms, conv kernels, embeddings, biases, and layer-scale gammas stay F32. Lossless on this model and consistently the fastest variant on CPU.
—Q8_0 — best size/accuracy tradeoff under F16; ~3× smaller than F32 with effectively identical detections.
—Q4_K — smallest practical quant. Rows with ne[0] % 256 != 0 (the decoder's 128-dim MLP halves, 60 tensors) silently fall back to Q80 per ggml's quantizer logic — net compression is still ~3.8× over F32. Use only when the size budget is tight; expect a measurable Recall@0.95 drop relative to F16/Q80 (see file table above).

Usage

bash

# 1. Clone + build rfdetr.cpp
git clone https://github.com/mudler/rf-detr.cpp
cd rt-detr.cpp
cmake -B build -DRFDETR_BUILD_CLI=ON && cmake --build build -j

# 2. Download a quant (F16 recommended)
hf download mudler/rfdetr-cpp-seg-nano rfdetr-seg-nano-f16.gguf --local-dir models/

# 3. Run segmentation (writes per-detection PNG masks to /tmp/seg_masks/)
build/bin/rfdetr-cli detect \
    --model models/rfdetr-seg-nano-f16.gguf \
    --input my_image.jpg \
    --threshold 0.5 --threads 8 \
    --masks /tmp/seg_masks \
    --output detections.json

Accuracy methodology

All accuracy metrics are computed against the upstream PyTorch reference (rfdetr 1.7.0) on 7 COCO val2017 images at threshold 0.5. Each detection match uses greedy Hungarian-style assignment by IoU (≥ 0.5 lenient, ≥ 0.95 strict) with class equality required.

Mask metrics are pixel-wise IoU between binary masks at the original image resolution (not the network's working resolution), after sigmoid + bicubic upsample of the per-query mask logits. Pixel agreement is the fraction of pixels where the C++ and PyTorch binary masks match.

See BENCHMARK.md and `benchmarks/results/accuracy_sweep.json` for the full sweep across all 32 (variant × quant) cells.

License

Apache-2.0 — matches the upstream rfdetr license.