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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 Large — GGUF for rfdetr.cpp
GGUF-format weights of [Roboflow RF-DETR Large](https://github.com/roboflow/rf-detr) (detection variant) for use with [rfdetr.cpp](https://github.com/adithyab94/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-large-f32.gguf` | F32 | 125.9 | 1.0000 | 1.0000 | 0.0018 | 236.6 |
| `rfdetr-large-f16.gguf` ← **recommended** | F16 | 68.2 | 1.0000 | 1.0000 | 0.0016 | 237.1 |
| `rfdetr-large-q8_0.gguf` | Q8_0 | 41.1 | 0.9731 | 0.9731 | 0.0035 | 251.2 |
| `rfdetr-large-q4_K.gguf` | Q4_K | 33.4 | 0.9088 | 0.8531 | 0.0184 | 272.0 |
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: 704×704
- Patch size: 16
- 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 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 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-large rfdetr-large-f16.gguf --local-dir models/
# 3. Run detection
build/bin/rfdetr-cli detect \
--model models/rfdetr-large-f16.gguf \
--input my_image.jpg \
--threshold 0.5 --threads 8 \
--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.
See [BENCHMARK.md](https://github.com/adithyab94/rf-detr.cpp/blob/main/BENCHMARK.md) and [`benchmarks/results/accuracy_sweep.json`](https://github.com/adithyab94/rf-detr.cpp/blob/main/benchmarks/results/accuracy_sweep.json) for the full sweep across the (variant × quant) cells.
## Provenance
- Source project: [Roboflow RF-DETR](https://github.com/roboflow/rf-detr)
- Upstream package: `rfdetr==1.9.0`
- Converted with [rfdetr.cpp](https://github.com/adithyab94/rf-detr.cpp) at commit [`fbef9387bed3`](https://github.com/adithyab94/rf-detr.cpp/commit/fbef9387bed3f3c82bf3f4e041253ffe3081f275)
- Checkpoint: official pretrained `rfdetr-large` weights (downloaded by the `rfdetr` package on first use)
### Checksums (SHA-256)
Also available as `SHA256SUMS` in this repo.
```
3cf3705985ac378e6a883dd31a6e837f89841c8602c511175dbdb14306b4baff rfdetr-large-f32.gguf
ac2feea7a9b5f9714d9d07e74d03dbe8aad8c8e90de29c472dd3a00d80f520a2 rfdetr-large-f16.gguf
e109a000768129f4c57b85f1486d94897a058db5e28b095f9a27239640b8df87 rfdetr-large-q8_0.gguf
ad3987c3729a06cb13190823650ba9f58458fdae49af0274d7e9732401af572e rfdetr-large-q4_K.gguf
```
## License
Apache-2.0 — matches the upstream [rfdetr](https://github.com/roboflow/rf-detr) license.
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