GREAT-IGEV (fastforks)

GREAT-IGEV (Li et al. 2025): IGEV-Stereo with global attention (spatial, epipolar-row matching and volume attention) that builds an excitation-weighted cost volume, then IGEV's ConvGRU updates, 32 iterations.

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/JarvisLee0423/GREAT-Stereo at 2a6b1efede8c.

Task: stereo. Tier: experimental. 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.

variant	weights (author link)	SHA-256	weights licence	commercial use
great-igev-eth3d@1	great-igev-eth3d-submit.pth	17b528b619af	Apache-2.0 (assumed from code)	yes
great-igev-kitti@1	great-igev-kitti-submit.pth	c80d31c62d93	Apache-2.0 (assumed from code)	yes
great-igev-middlebury@1	great-igev-middlebury-submit.pth	2f11060a7187	Apache-2.0 (assumed from code)	yes
great-igev-sceneflow@1	great-igev-sceneflow-submit.pth	dd131ef44fc0	Apache-2.0 (assumed from code)	yes

Licence

Original-code strings, recorded as they are (research-use package; check them before any other use). Code: Apache-2.0. Files: LICENSE.

Code licence: Apache-2.0; the authors do not state a separate weights licence.

Training data: Freiburg SceneFlow (FlyingThings3D, Driving, Monkaa).

Licence evidence: https://github.com/JarvisLee0423/GREAT-Stereo/blob/2a6b1efede8c3a9eec9540790c2700ea2f3dee7d/README.md#L558

Input size

any H x W >= 32, padded to a multiple of 32 Tested up to (portable, fp16, RTX 5060 Ti 16 GB): great-igev-eth3d not measured yet; great-igev-kitti not measured yet; great-igev-middlebury not measured yet; great-igev-sceneflow not measured yet

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
great-igev-eth3d	2252.41	1458.32	1084.72	393.14	3.7x	2.8x	8861 / 9206	1402 / 1678	1192 / 1564
great-igev-middlebury (max_disp 768)	–	–	1785.35	578.81	–	3.1x	–	1875 / 2078	1553 / 1744

Startup and first call

model	load (s): original	portable	kernels	first call (s): original	portable	kernels
great-igev-eth3d	0.44 (375x1242)	0.32	0.23†	4.60 (375x1242)	1.10†	0.40†
great-igev-middlebury (max_disp 768)	–	3.91	0.61†	–	1.81†	0.59†

Fixed-size speed and peak memory, all models

model	mode	640x480	1280x720	1920x1080	12 MP 4000x3000
great-igev-eth3d	portable	206.7 ms / 681 / 1030	627.7 ms / 1216 / 2262	1532.6 ms / 1688 / 2780	13067.4 ms (n=3) / 11364 / 15106
great-igev-eth3d	kernels	99.0 ms / 385 / 476	241.2 ms / 1041 / 1338	544.2 ms / 1506 / 2354	3753.5 ms (n=3) / 11736 / 15500
great-igev-kitti	portable	217.1 ms / 681 / 1030	628.2 ms / 1216 / 2262	1532.7 ms / 1688 / 2780	13065.3 ms (n=3) / 11364 / 15106
great-igev-kitti	kernels	93.2 ms / 385 / 476	240.9 ms / 1041 / 1338	544.6 ms / 1506 / 2356	3755.4 ms (n=3) / 11736 / 15500
great-igev-middlebury (max_disp 768)	portable	328.5 ms / 1082 / 1888	1025.5 ms / 1344 / 2262	2554.0 ms (n=3) / 2549 / 4266	OOM
great-igev-middlebury (max_disp 768)	kernels	131.4 ms / 942 / 1258	356.0 ms / 1279 / 1906	804.4 ms / 1825 / 2656	6324.4 ms (n=3) / 11920 / 15500
great-igev-sceneflow	portable	200.9 ms / 681 / 1030	624.1 ms / 1216 / 2262	1531.8 ms / 1688 / 2780	13071.3 ms (n=3) / 11364 / 15106
great-igev-sceneflow	kernels	93.5 ms / 385 / 476	241.1 ms / 1041 / 1338	545.3 ms / 1506 / 2354	3759.3 ms (n=3) / 11736 / 15500

Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).

• great-igev-kitti, great-igev-sceneflow: same network and settings as great-igev-eth3d, measured once (rows not repeated).

Hardware, protocol and caveats: benchmarks (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 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.

Usage

from fastforks import stereo

model = stereo.load("great-igev-eth3d")  # portable PyTorch on the GPU if there is one, else the CPU
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: great-igev-eth3d, great-igev-kitti, great-igev-middlebury, great-igev-sceneflow.

Kernels backend

stereo.load("great-igev-eth3d", mode="kernels") runs our CUDA kernels (igev, igevpp, cutlass_conv, cudnn_hacc). 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.