--- library_name: "fastforks" pipeline_tag: "object-detection" license: "apache-2.0" tags: - "detection" - "object-detection" - "instance-segmentation" - "cuda" - "pytorch" - "fastforks" --- # D-FINE (HGNetV2, N / S / M / L / X) (fastforks) D-FINE (Peng et al. 2025, USTC): real-time end-to-end detection like RT-DETR (HGNetV2 backbone, hybrid encoder, deformable-attention decoder over 300 selected queries, no NMS) whose decoder layers refine every box edge as a distribution over distances (fine-grained distribution refinement); boxes, scores and COCO classes at the input size. The portable `module()` is the whole network at 640x640 (class logits and normalised boxes). This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: . Kernels for the `kernels` backend: . Original code: at `956d1709314c`. Author weight repos: , , , , . Task: detection. Tier: stable. Outputs: `boxes`, `scores`, `labels`. Backends: `portable`, `kernels`. Input: one RGB image. Output: boxes, class labels, scores and instance masks. ## Variants Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in [`card.toml`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/detection/models/dfine/card.toml). | variant | weights (author link) | SHA-256 | weights licence | commercial use | |---|---|---|---|---| | `dfine-l-coco@1` | [dfine-large-coco.safetensors](https://huggingface.co/ustc-community/dfine-large-coco/resolve/aa8f8211545e4cff5adf52ea512f9ac74a9824ec/model.safetensors) | `4e9d77ba937a` | Apache-2.0 | yes | | `dfine-m-coco@1` | [dfine-medium-coco.safetensors](https://huggingface.co/ustc-community/dfine-medium-coco/resolve/4ab9f5e466432f4fcf9a9a023da6aa5ecc9c9829/model.safetensors) | `5a53154fb87a` | Apache-2.0 | yes | | `dfine-n-coco@1` | [dfine-nano-coco.safetensors](https://huggingface.co/ustc-community/dfine-nano-coco/resolve/066438d3d8f0da137a37b38fdf3368fd4afceced/model.safetensors) | `19e06bdc873d` | Apache-2.0 | yes | | `dfine-s-coco@1` | [dfine-small-coco.safetensors](https://huggingface.co/ustc-community/dfine-small-coco/resolve/f79e65b5fbb33ceb9d3ebba042955d7410c608f8/model.safetensors) | `a144baff3a27` | Apache-2.0 | yes | | `dfine-x-coco@1` | [dfine-xlarge-coco.safetensors](https://huggingface.co/ustc-community/dfine-xlarge-coco/resolve/ea4f6be7350bbe3c199ec6febc74168346cb5a68/model.safetensors) | `3ac6ea37c6c9` | Apache-2.0 | yes | ## Licence Original-code strings, recorded as they are (research-use package; check them before any other use). Code: Apache-2.0. Files: `LICENSE`. Training data: COCO 2017 (train, instance annotations), ImageNet-1k (ILSVRC 2012 classification) (COCO 2017 train; the HGNetV2 backbones start from ImageNet-1k pretraining). Licence evidence: ## Input size any H x W >= 32, padded to a multiple of 32 Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `dfine-l-coco` 3000x4000 (0.5 GiB peak allocated, 0.7 reserved); `dfine-m-coco` 3000x4000 (0.5 GiB peak allocated, 0.6 reserved); `dfine-n-coco` 3000x4000 (0.5 GiB peak allocated, 0.6 reserved); `dfine-s-coco` 3000x4000 (0.5 GiB peak allocated, 0.6 reserved); `dfine-x-coco` 3000x4000 (0.6 GiB peak allocated, 0.7 reserved) ## Speed and memory ### Detection, synthetic, 800x1344 | model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `dfine-l-coco` | 37.1 | 44.9 | 16.2 | 8.70 | 4.3x | 1.9x | 278 / 350 | 152 / 172 | 179 / 300 | | `dfine-m-coco` | 30.8 | 38.2 | 13.5 | 5.97 | 5.2x | 2.3x | 209 / 256 | 126 / 142 | 156 / 252 | | `dfine-n-coco` | 24.3 | 29.1 | 10.3 | 2.72 | 8.9x | 3.8x | 92 / 126 | 88 / 116 | 124 / 202 | | `dfine-s-coco` | 25.7 | 31.9 | 11.1 | 4.04 | 6.4x | 2.7x | 152 / 184 | 105 / 128 | 138 / 212 | | `dfine-x-coco` | 44.9 | 54.6 | 18.5 | 14.1 | 3.2x | 1.3x | 476 / 536 | 227 / 258 | 237 / 364 | ### Startup and first call | model | load (s): original | portable | kernels | first call (s): original | portable | kernels | | --- | --- | --- | --- | --- | --- | --- | | `dfine-l-coco` | 3.68 | 1.09 | 1.46 | 1.41 | 0.67 | 0.47 | | `dfine-m-coco` | 3.63 | 1.04 | 1.20 | 1.55 | 0.69 | 0.56 | | `dfine-n-coco` | 5.24 | 1.25 | 1.21 | 4.07 | 0.85 | 0.62 | | `dfine-s-coco` | 2.99 | 1.05 | 1.16 | 1.44 | 0.71 | 0.52 | | `dfine-x-coco` | 3.88 | 1.39 | 1.41 | 1.45 | 0.65 | 0.45 | ### torch.compile and TensorRT | model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `dfine-n-coco` | 800x1344 | 10.3 | 5.4 | 26.7 | 1.4 | 51 | 2.72 | 88 / 116 | 101 / 118 | 94 | Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB). Hardware, protocol and caveats: [benchmarks](https://github.com/kleinicke/fastforks/blob/main/docs/benchmarks.md) (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or `(fp32)` where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's. ## Accuracy ### Parity with the original code Every variant is checked against reference outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.2 and p99 <= 1.0 (absolute error of the output `boxes`). ## Limitations - The original in the benchmarks and goldens is the Hugging Face transformers 5.19.0 port (DFineForObjectDetection, ustc-community weights) of the official D-FINE (Apache-2.0); the official repository code itself was not run. ## Usage ```python from fastforks import detection model = detection.load("dfine-l-coco") # mode="auto": the preferred backend from recorded measurements result = model("photo.jpg") result.boxes, result.scores, result.labels # [N, 4], [N], [N] result.backend # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16') ``` Install with `pip install fastforks` (see ). Other variants: `dfine-l-coco`, `dfine-m-coco`, `dfine-n-coco`, `dfine-s-coco`, `dfine-x-coco`. ## Kernels backend `detection.load("dfine-l-coco", mode="kernels")` runs our CUDA kernels (`cudnn_hacc`). It needs a prebuilt kernel for your GPU architecture (downloaded from on first use) and a recent NVIDIA driver; if there is none it raises `BackendUnavailableError` with the reason and never falls back silently. `mode="auto"` picks `kernels` when it can run and logs the choice.