You can deploy this model and start earning money today!

adithya-balaji/
rfdetr-cpp-seg-xlarge

Name: rfdetr-cpp-seg-xlarge
Availability: InStock

instance-segmentation

apache-2.0

Views

No views yet

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

GGUF-format weights of Roboflow RF-DETR Seg-XLarge (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-xlarge-f32.gguf`	F32	141.9	0.9821	0.9821	0.9964	0.9999	1197.3
`rfdetr-seg-xlarge-f16.gguf` ← recommended	F16	76.7	0.9821	0.9821	0.9965	0.9999	1183.2
`rfdetr-seg-xlarge-q8_0.gguf`	Q8_0	46.1	1.0000	1.0000	0.9941	0.9998	1086.5
`rfdetr-seg-xlarge-q4_K.gguf`	Q4_K	36.6	0.9049	0.6718	0.9726	0.9994	1157.5

All accuracy numbers above are computed against the upstream PyTorch reference (rfdetr 1.9.0) on 7 images (000000000139.jpg, 000000000632.jpg, 000000039769.jpg, 000000087038.jpg, 000000252219.jpg, 000000397133.jpg, bus.jpg) at threshold 0.5. Latency is measured separately with rfdetr-cli bench (8 iters + 3 warmup) at T=8 threads on a single Intel Core i7-12800HX, on tests/fixtures/ci/test_image.jpg.

Architecture

Backbone: DINOv2-small
Input resolution: 624×624
Patch size: 12
Decoder layers: 6
Object queries: 300
Task: instance segmentation (boxes + per-query masks)
Mask resolution: 156×156 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 Q8_0 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/Q8_0 (see file table above).

Compatibility

These GGUFs stamp rfdetr.preprocess.resize_mode = "bilinear_no_antialias", matching RF-DETR 1.9's antialias-free float bilinear resize (align_corners=false, half-pixel coordinates, no intermediate uint8 rounding). rf-detr.cpp treats this key as optional: GGUFs that predate this metadata (no resize_mode key) keep using the legacy stb-based resize path, so older files continue to produce their original outputs unchanged. An unrecognized resize_mode value is rejected rather than guessed.

Keypoint-preview inference is not supported. rf-detr.cpp does not implement the keypoint output head; this repository only serves box detection + instance segmentation masks outputs.

Usage

bash

# 1. Clone + build rfdetr.cpp
git clone https://github.com/adithyab94/rf-detr.cpp
cd rf-detr.cpp
cmake -B build -DRFDETR_BUILD_CLI=ON && cmake --build build -j

# 2. Download a quant (F16 recommended)
hf download adithya-balaji/rfdetr-cpp-seg-xlarge rfdetr-seg-xlarge-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-xlarge-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 1.9.0) on 7 images (000000000139.jpg, 000000000632.jpg, 000000039769.jpg, 000000087038.jpg, 000000252219.jpg, 000000397133.jpg, bus.jpg) 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 the (variant × quant) cells.

Provenance

Source project: Roboflow RF-DETR
Upstream package: rfdetr==1.9.0
Converted with rfdetr.cpp at commit fbef9387bed3
Checkpoint: official pretrained rfdetr-seg-xlarge weights (downloaded by the rfdetr package on first use)

Checksums (SHA-256)

Also available as SHA256SUMS in this repo.

77c3778b4421640ceb21d94f458dd85555bdc72f1e0c71c13b854d74292d01d1  rfdetr-seg-xlarge-f32.gguf
72b6210e255ebb89a8d471c06a6f4b5307205b1a34b9e4292de90d1488da4e26  rfdetr-seg-xlarge-f16.gguf
524baa285dd6329cd8a033d4ca575a9480370cc34ba9d2b5f2c58ae1b1a01348  rfdetr-seg-xlarge-q8_0.gguf
ffa159749aac5b9eb172b9a2fd62f66ed1f5adcafacafcb5bd010b847b641597  rfdetr-seg-xlarge-q4_K.gguf

License

Apache-2.0 — matches the upstream rfdetr license.