--- library_name: "fastforks" pipeline_tag: "depth-estimation" license: "other" license_name: "gpl-3.0-only-code-weights-unstated" license_link: "https://github.com/antabangun/coex/blob/a98b37d33f37754582b1e71f38216af8c69f2347/LICENSE" tags: - "stereo" - "stereo-matching" - "disparity" - "cuda" - "pytorch" - "fastforks" --- # CoEx (fastforks) CoEx (Bangunharcana et al. 2021): MobileNetV2 features, an attention-weighted correlation volume, 3-D aggregation with guided cost excitation, top-2 soft-argmax and superpixel upsampling (max disparity 192). 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 `a98b37d33f37`. Task: stereo. Tier: stable. Outputs: `disparity`. Backends: `portable`, `kernels`. Input: a rectified left / right image pair. Output: a disparity map (pixels, left view). ## 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/stereo/models/coex/card.toml). | variant | weights (author link) | SHA-256 | weights licence | commercial use | |---|---|---|---|---| | `coex-sceneflow@1` | [sceneflow.ckpt](https://www.dropbox.com/s/c1v2r74tlbrrmsr/sceneflow.ckpt?dl=1) | `e618be2d75cd` | GPL-3.0-only (assumed from code) | yes | ## Licence Original-code strings, recorded as they are (research-use package; check them before any other use). Code: GPL-3.0-only. Files: `LICENSE`. Code licence: GPL-3.0-only; the authors do not state a separate weights licence. Training data: Freiburg SceneFlow (FlyingThings3D, Driving, Monkaa). - Licence note: The code is GPL-3.0 (copyleft) and ships in the separate fastforks-nc distribution (`pip install 'fastforks-core[nc]'`); the authors state no weights licence. 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): `coex-sceneflow` 3000x4000 (8.5 GiB peak allocated, 10.7 reserved) ## Speed and memory ### Stereo, Middlebury 2014 half, 994x1482 | model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `coex-sceneflow` | 94.9 | 60.0 | 49.93 | 23.48 | 2.6x | 2.1x | 2098 / 2160 | 1136 / 1254 | 495 / 886 | ### Startup and first call | model | load (s): original | portable | kernels | first call (s): original | portable | kernels | | --- | --- | --- | --- | --- | --- | --- | | `coex-sceneflow` | – | 0.16 | 0.09† | 0.63 (375x1242) | 0.06† | 0.04† | ### 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) | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `coex-sceneflow` | 994x1482 | 49.93 | 29.4 | 22.4 | 20.6 | 57 | 23.48 | 1136 / 1254 | 351 / 452 | 360 | ### Fixed-size speed and peak memory, all models | model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 | | --- | --- | --- | --- | --- | --- | | `coex-sceneflow` | portable | 11.2 ms / 225 / 312 | 28.5 ms / 671 / 890 | 70.0 ms / 1485 / 1916 | 554.7 ms / 8660 / 10964 | | `coex-sceneflow` | kernels | 4.3 ms / 103 / 322 | 13.7 ms / 256 / 400 | 32.9 ms / 559 / 804 | 211.1 ms / 3190 / 4410 | 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 ### On public datasets Original = the authors' code, same checkpoint and protocol. | variant | dataset | original | portable | kernels | note | |---|---|---|---|---|---| | `coex-sceneflow` | ETH3D two-view train, bad > 1 px % / EPE | 33.46 / 15.994 | 33.46 / 15.989 | 33.46 / 16.000 | | | `coex-sceneflow` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 32.24 / 7.418 | 32.32 / 7.427 | 32.31 / 7.421 | | Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-10-27. ### Parity with the original code Every variant is checked against golden 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| <= 0.02 px and p99 <= 0.25 px. ## Limitations - SceneFlow-only weights: zero-shot output on real images is poor, so this is mainly a latency reference. - The original code's KITTI weights (in its git repository) are not included. ## Usage ```python from fastforks import stereo model = stereo.load("coex-sceneflow") # mode="auto": the preferred backend from recorded measurements result = model("left.png", "right.png") result.disparity # [H, W] float32 tensor, input resolution result.backend # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16') ``` Install with `pip install fastforks` (see ). Other variants: `coex-sceneflow`. ## Kernels backend `stereo.load("coex-sceneflow", mode="kernels")` runs our CUDA kernels (`igev`, `cutlass_conv`, `cudnn_hacc`, `stereo_ops`). 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.