| --- |
| 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: <https://github.com/kleinicke/fastforks>. Kernels for the `kernels` backend: <https://huggingface.co/fastforks/kernels>. Original code: <https://github.com/antabangun/coex> 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: <https://github.com/antabangun/coex/blob/a98b37d33f37754582b1e71f38216af8c69f2347/LICENSE> |
|
|
| ## 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 | 49.9 | 23.5 | 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 | 49.9 | 29.4 | 22.4 | 20.6 | 57 | 23.5 | 1136 / 1254 | 351 / 452 | 360 | |
|
|
| ### Fixed-size speed and peak memory, all models |
|
|
| | model | mode | 480x640 | 720x1280 | 1080x1920 | 12 MP 3000x4000 | |
| | --- | --- | --- | --- | --- | --- | |
| | `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 fp32 | original fp16 | portable | kernels | note | |
| |---|---|---|---|---|---|---| |
| | `coex-sceneflow` | ETH3D two-view train, bad > 1 px % / EPE | 33.43 / 16.021 | 33.46 / 15.995 | 33.46 / 15.989 | 33.46 / 16.000 | | |
| | `coex-sceneflow` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 32.25 / 7.417 | 32.24 / 7.418 | 32.32 / 7.428 | 32.31 / 7.421 | | |
| |
| Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-05. |
| |
| ### 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| <= 0.02 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 <https://github.com/kleinicke/fastforks>). 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 <https://huggingface.co/fastforks/kernels> 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. |
|
|
| <!-- staged from fastforks feccac41 (2026-11-06) --> |
|
|
