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GGUF-format weights of Roboflow RF-DETR Medium (detection variant) for use with rfdetr.cpp, a C++/ggml implementation that matches the upstream PyTorch model on CPU.
Fuente del modelo
Descripción de la fuente
GGUF-format weights of Roboflow RF-DETR Medium (detection 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.
Fuentes
1 fuenteVerificado 27 sept
Artefactos del modelo
4 artefactosExtractos de fuentes
2 extractos| File | Quant | Size (MB) | Recall @ IoU 0.5 | Recall @ IoU 0.95 | Mean |Δscore| | Latency (median ms, T=8) |
|---|---|---|---|---|---|---|
rfdetr-medium-f32.gguf | F32 | 125.0 | 0.9389 | 0.8937 | 0.0065 | 139.9 |
rfdetr-medium-f16.gguf ← recommended | F16 | 67.2 | 0.9389 | 0.9032 | 0.0067 | 143.9 |
rfdetr-medium-q8_0.gguf | Q8_0 | 40.2 | 0.9389 | 0.9032 | 0.0083 | 145.8 |
rfdetr-medium-q4_K.gguf | Q4_K | 32.5 |
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).
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 [email protected] drop relative to F16/Q8_0 (see file table above).# 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-medium rfdetr-medium-f16.gguf --local-dir models/
# 3. Run detection
build/bin/rfdetr-cli detect \
--model models/rfdetr-medium-f16.gguf \
--input my_image.jpg \
--threshold 0.5 --threads 8 \
--output detections.json
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.
See BENCHMARK.md and benchmarks/results/accuracy_sweep.json for the full sweep across all 32 (variant × quant) cells.
Apache-2.0 — matches the upstream rfdetr license.
rfdetr-medium-q4_K.gguf
gguf · 32,5 MB · SHA-256 74f00fdee5e5…3f34 · Hugging Face
--- license: apache-2.0 library_name: rfdetr.cpp tags: - object-detection - rfdetr - gguf - ggml - cpp-inference pipeline_tag: object-detection base_model: roboflow/rfdetr --- # RF-DETR Medium — GGUF for rfdetr.cpp GGUF-format weights of [Roboflow RF-DETR Medium](https://github.com/roboflow/rf-detr) (detection variant) for use with [rfdetr.cpp](https://github.com/mudler/rf-detr.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 \|Δscore\| | Latency (median ms, T=8) | |---|---|---:|---:|---:|---:|---:| | `rfdetr-medium-f32.gguf` | F32 | 125.0 | 0.9389 | 0.8937 | 0.0065 | 139.9 | | `rfdetr-medium-f16.gguf` ← **recommended** | F16 | 67.2 | 0.9389 | 0.9032 | 0.0067 | 143.9 | | `rfdetr-medium-q8_0.gguf` | Q8_0 | 40.2 | 0.9389 | 0.9032 | 0.0083 | 145.8 | | `rfdetr-medium-q4_K.gguf` | Q4_K | 32.5 | 0.8992 | 0.7714 | 0.0199 | 155.8 | 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: 576×576 - Patch size: 14 - Decoder layers: 4 - Object queries: 300 - Task: object detection (boxes only) ## 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 [email protected] drop relative to F16/Q8_0 (see file table above). ## Usage ```b...
Source context: 623 downloads · 0 likes · Pipeline object-detection · Library rfdetr.cpp · Repo mudler/rfdetr-cpp-medium
| 0.8992 |
| 0.7714 |
| 0.0199 |
| 155.8 |