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fastforks
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selective-igev

Depth Estimation
PyTorch
fastforks
stereo
stereo-matching
disparity
cuda
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selective-igev / README.md
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metadata 
library_name: fastforks
pipeline_tag: depth-estimation
license: other
license_name: mit-code-weights-unstated
license_link: >-
  https://github.com/Windsrain/Selective-Stereo/blob/c39acbfef9fbe36fbc01efa4abeafc62bbe2da36/README.md#L25
tags:
  - stereo
  - stereo-matching
  - disparity
  - cuda
  - pytorch
  - fastforks

Selective-IGEV (fastforks)

Selective-IGEV (Wang et al. 2024, Selective-Stereo): IGEV-Stereo with selective recurrent units, attention-blended small (1x1) and large (3x3) ConvGRUs on a channel- and spatially re-weighted context, 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/Windsrain/Selective-Stereo at c39acbfef9fb.

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
selective-igev-eth3d-finetune@1 eth3d_finetune.pth 29d62b91c58e MIT (assumed from code) yes
selective-igev-eth3d-train@1 eth3d_train.pth 989327ad0220 MIT (assumed from code) yes
selective-igev-kitti12@1 kitti12.pth 3a9ed6f2e778 MIT (assumed from code) yes
selective-igev-kitti15@1 kitti15.pth 488254b5cfbf MIT (assumed from code) yes
selective-igev-middlebury-finetune@1 middlebury_finetune.pth ce019e97bbe8 MIT (assumed from code) yes
selective-igev-middlebury-train@1 middlebury_train.pth a51f67029d77 MIT (assumed from code) yes
selective-igev-sceneflow@1 sceneflow.pth 27286b184d68 MIT (assumed from code) yes

Licence

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

Code licence: MIT; the authors do not state a separate weights licence.

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

Licence evidence: https://github.com/Windsrain/Selective-Stereo/blob/c39acbfef9fbe36fbc01efa4abeafc62bbe2da36/README.md#L25

Input size

any H x W >= 32, padded to a multiple of 32 Tested up to (portable, fp16, RTX 5060 Ti 16 GB): selective-igev-eth3d-finetune 3000x4000 (10.3 GiB peak allocated, 14.0 reserved); selective-igev-eth3d-train 3000x4000 (10.3 GiB peak allocated, 14.0 reserved); selective-igev-kitti12 3000x4000 (10.3 GiB peak allocated, 14.0 reserved); selective-igev-kitti15 3000x4000 (10.3 GiB peak allocated, 14.0 reserved); selective-igev-middlebury-finetune 3000x4000 (10.3 GiB peak allocated, 14.0 reserved); selective-igev-middlebury-train 3000x4000 (10.3 GiB peak allocated, 14.0 reserved); selective-igev-sceneflow 3000x4000 (10.3 GiB peak allocated, 14.0 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
selective-igev-eth3d-finetune 2201.3 1455.0 998.4 348.76 4.2x 2.9x 2013 / 2152 1238 / 1488 1109 / 1390

Startup and first call

model load (s): original portable kernels first call (s): original portable kernels
selective-igev-eth3d-finetune 0.36 (375x1242) 0.50 0.14† 2.25 (375x1242) 1.02† 0.35†

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)
selective-igev-eth3d-finetune 994x1482 998.4 690.7 125.8 460.8 256 348.76 1238 / 1488 919 / 1088 1148

Fixed-size speed and peak memory, all models

model mode 640x480 1280x720 1920x1080 12 MP 4000x3000
selective-igev-eth3d-finetune portable 190.5 ms / 282 / 362 575.3 ms / 769 / 1036 1420.2 ms / 1690 / 2030 8918.7 ms (n=3) / 10549 / 14320
selective-igev-eth3d-finetune kernels 82.5 ms / 260 / 368 212.8 ms / 693 / 1010 472.5 ms / 1502 / 2046 2806.7 ms (n=3) / 6928 / 8800
selective-igev-eth3d-train portable 190.5 ms / 282 / 362 575.3 ms / 769 / 1036 1420.4 ms / 1690 / 2030 8919.8 ms (n=3) / 10549 / 14320
selective-igev-eth3d-train kernels 82.4 ms / 260 / 368 212.8 ms / 693 / 1010 472.6 ms / 1502 / 2046 2807.0 ms (n=3) / 6928 / 8800
selective-igev-kitti12 portable 190.6 ms / 282 / 362 575.3 ms / 769 / 1036 1420.5 ms / 1690 / 2030 8917.7 ms (n=3) / 10549 / 14320
selective-igev-kitti12 kernels 82.3 ms / 260 / 368 212.9 ms / 693 / 1010 471.9 ms / 1502 / 2046 2803.4 ms (n=3) / 6928 / 8800
selective-igev-kitti15 portable 190.6 ms / 282 / 362 575.3 ms / 769 / 1036 1420.8 ms / 1690 / 2030 8918.8 ms (n=3) / 10549 / 14320
selective-igev-kitti15 kernels 82.5 ms / 260 / 368 212.8 ms / 693 / 1010 471.9 ms / 1502 / 2046 2803.9 ms (n=3) / 6928 / 8800
selective-igev-middlebury-finetune portable 190.6 ms / 282 / 362 575.6 ms / 769 / 1036 1420.8 ms / 1690 / 2030 8923.1 ms (n=3) / 10549 / 14320
selective-igev-middlebury-finetune kernels 82.3 ms / 260 / 368 212.6 ms / 693 / 1010 472.1 ms / 1502 / 2046 2804.4 ms (n=3) / 6928 / 8800
selective-igev-middlebury-train portable 190.7 ms / 282 / 362 575.4 ms / 769 / 1036 1420.5 ms / 1690 / 2030 8921.4 ms (n=3) / 10549 / 14320
selective-igev-middlebury-train kernels 82.5 ms / 260 / 368 213.1 ms / 693 / 1010 472.4 ms / 1502 / 2046 2804.8 ms (n=3) / 6928 / 8800
selective-igev-sceneflow portable 190.6 ms / 282 / 362 575.4 ms / 769 / 1036 1420.3 ms / 1690 / 2030 8917.6 ms (n=3) / 10549 / 14320
selective-igev-sceneflow kernels 82.5 ms / 260 / 368 213.5 ms / 693 / 1010 473.3 ms / 1502 / 2046 2808.4 ms (n=3) / 6928 / 8800

Apple silicon (MPS)

model portable kernels speed-up
selective-igev-sceneflow 2292 ms / 1727 MiB (float16) 1878 ms / 1704 MiB 1.22x

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

  • selective-igev-eth3d-train, selective-igev-kitti12, selective-igev-kitti15, selective-igev-middlebury-finetune, selective-igev-middlebury-train, selective-igev-sceneflow: same network and settings as selective-igev-eth3d-finetune, 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

On public datasets

Original = the authors' code, same checkpoint and protocol.

variant dataset original portable kernels note
selective-igev-sceneflow ETH3D two-view train, bad > 1 px % / EPE 6.07 / 1.430 6.05 / 1.407 6.07 / 1.415
selective-igev-sceneflow Middlebury 2014 trainingH, bad > 2 px % / EPE 11.78 / 2.584 11.80 / 2.587 11.79 / 2.585
selective-igev-kitti12 ETH3D two-view train, bad > 1 px % / EPE 24.07 / 3.647 24.07 / 3.660
selective-igev-kitti12 Middlebury 2014 trainingH, bad > 2 px % / EPE 20.08 / 4.845 20.10 / 4.862
selective-igev-kitti15 ETH3D two-view train, bad > 1 px % / EPE 24.26 / 2.442 24.26 / 2.447
selective-igev-kitti15 Middlebury 2014 trainingH, bad > 2 px % / EPE 19.65 / 4.956 19.64 / 4.942
selective-igev-middlebury-finetune ETH3D two-view train, bad > 1 px % / EPE 24.26 / 0.995 24.24 / 0.995
selective-igev-middlebury-finetune Middlebury 2014 trainingH, bad > 2 px % / EPE 5.66 / 1.208 5.66 / 1.192 trained on this dataset's public images; not a zero-shot result
selective-igev-middlebury-train ETH3D two-view train, bad > 1 px % / EPE 10.24 / 0.433 10.26 / 0.433
selective-igev-middlebury-train Middlebury 2014 trainingH, bad > 2 px % / EPE 5.95 / 1.434 5.99 / 1.436 trained on this dataset's public images; not a zero-shot result
selective-igev-eth3d-finetune ETH3D two-view train, bad > 1 px % / EPE 0.32 / 0.082 0.32 / 0.082 trained on this dataset's public images; not a zero-shot result
selective-igev-eth3d-finetune Middlebury 2014 trainingH, bad > 2 px % / EPE 12.33 / 2.811 12.35 / 2.811
selective-igev-eth3d-train ETH3D two-view train, bad > 1 px % / EPE 0.78 / 0.137 0.78 / 0.137 trained on this dataset's public images; not a zero-shot result
selective-igev-eth3d-train Middlebury 2014 trainingH, bad > 2 px % / EPE 7.99 / 2.162 8.01 / 2.156

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

  • ONNX export needs torch >= 2.14 (older exporters fail on this network).

Usage 

from fastforks import stereo

model = stereo.load("selective-igev-eth3d-finetune")  # 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: selective-igev-eth3d-finetune, selective-igev-eth3d-train, selective-igev-kitti12, selective-igev-kitti15, selective-igev-middlebury-finetune, selective-igev-middlebury-train, selective-igev-sceneflow.

Kernels backend

stereo.load("selective-igev-eth3d-finetune", mode="kernels") runs our CUDA kernels (igev, 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.