Fast-ACVNet (fastforks)

Fast-ACVNet (Xu et al. 2022): MobileNetV2 features; attention weights from a correlation volume (1/8 group-wise, propagated to 1/4; Fast-ACVNet+: 1/4) select the top-24 disparities of a concatenation volume, then a 3-D hourglass, 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/gangweix/Fast-ACVNet at 61d5375e1816.

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
fast-acvnet-generalization@1	Fast-ACVNet/generalization.ckpt	b9d06b9d3df3	MIT (assumed from code)	yes
fast-acvnet-kitti12@1	Fast-ACVNet/kitti_2012.ckpt	221b21107e60	MIT (assumed from code)	yes
fast-acvnet-kitti15@1	Fast-ACVNet/kitti_2015.ckpt	b7d8c609b8b2	MIT (assumed from code)	yes
fast-acvnet-plus-generalization@1	Fast-ACVNet+/generalization.ckpt	bd2f79680519	MIT (assumed from code)	yes
fast-acvnet-plus-kitti12@1	Fast-ACVNet+/kitti_2012.ckpt	e3ce45f2dc62	MIT (assumed from code)	yes
fast-acvnet-plus-kitti15@1	Fast-ACVNet+/kitti_2015.ckpt	bf4c8ef707eb	MIT (assumed from code)	yes
fast-acvnet-plus-sceneflow@1	Fast-ACVNet+/sceneflow.ckpt	1a1328828baf	MIT (assumed from code)	yes
fast-acvnet-sceneflow@1	Fast-ACVNet/sceneflow.ckpt	ee7ca89926f7	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/gangweix/Fast-ACVNet/blob/61d5375e1816f124f4417cea506eeaddac1504e4/LICENSE.md

Input size

any H x W >= 32, padded to a multiple of 32 Tested up to (portable, fp16, RTX 5060 Ti 16 GB): fast-acvnet-generalization 3000x4000 (5.0 GiB peak allocated, 7.9 reserved); fast-acvnet-kitti12 3000x4000 (5.0 GiB peak allocated, 7.9 reserved); fast-acvnet-kitti15 3000x4000 (5.0 GiB peak allocated, 7.9 reserved); fast-acvnet-plus-generalization 3000x4000 (4.9 GiB peak allocated, 7.9 reserved); fast-acvnet-plus-kitti12 3000x4000 (4.9 GiB peak allocated, 7.9 reserved); fast-acvnet-plus-kitti15 3000x4000 (4.9 GiB peak allocated, 7.9 reserved); fast-acvnet-plus-sceneflow 3000x4000 (4.9 GiB peak allocated, 7.9 reserved); fast-acvnet-sceneflow 3000x4000 (5.0 GiB peak allocated, 7.9 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
fast-acvnet-generalization	101	60.6	54.7	38.2	1.6x	1.4x	1421 / 1506	664 / 736	739 / 1042
fast-acvnet-plus-generalization	138	80.9	70.6	46.4	1.7x	1.5x	1441 / 1522	662 / 756	790 / 1120

Startup and first call

model	load (s): original	portable	kernels	first call (s): original	portable	kernels
fast-acvnet-generalization	0.24 (375x1242)	0.18	0.20†	1.73 (375x1242)	0.08†	0.04†
fast-acvnet-plus-generalization	0.23 (375x1242)	0.10	0.10†	0.04 (375x1242)	0.07†	0.05†

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)
fast-acvnet-sceneflow	994x1482	54.7	36.2	41.8	36.7	71	38.2	664 / 736	584 / 698	484

Fixed-size speed and peak memory, all models

model	mode	480x640	720x1280	1080x1920	12 MP 3000x4000
fast-acvnet-generalization	portable	13.9 ms / 137 / 208	31.1 ms / 402 / 664	78.3 ms / 890 / 1418	492.1 ms / 5077 / 8074
fast-acvnet-generalization	kernels	6.8 ms / 169 / 582	22.0 ms / 435 / 608	55.7 ms / 954 / 1372	357.3 ms / 5445 / 7618
fast-acvnet-kitti12	portable	14.0 ms / 137 / 208	31.1 ms / 402 / 664	78.2 ms / 890 / 1418	492.4 ms / 5077 / 8074
fast-acvnet-kitti12	kernels	6.8 ms / 169 / 582	22.0 ms / 435 / 608	55.6 ms / 954 / 1372	357.3 ms / 5445 / 7618
fast-acvnet-kitti15	portable	13.9 ms / 137 / 208	31.1 ms / 402 / 664	78.2 ms / 890 / 1418	492.4 ms / 5077 / 8074
fast-acvnet-kitti15	kernels	6.8 ms / 169 / 582	22.0 ms / 435 / 608	55.6 ms / 954 / 1372	357.3 ms / 5445 / 7618
fast-acvnet-plus-generalization	portable	14.3 ms / 136 / 208	38.0 ms / 401 / 664	99.4 ms / 887 / 1418	623.6 ms / 5054 / 8076
fast-acvnet-plus-generalization	kernels	7.8 ms / 169 / 546	25.3 ms / 457 / 598	66.2 ms / 1006 / 1226	422.5 ms / 5743 / 7342
fast-acvnet-plus-kitti12	portable	14.4 ms / 136 / 208	38.0 ms / 401 / 664	99.4 ms / 887 / 1418	623.3 ms / 5054 / 8076
fast-acvnet-plus-kitti12	kernels	7.7 ms / 169 / 546	25.3 ms / 457 / 598	66.2 ms / 1006 / 1226	422.4 ms / 5743 / 7342
fast-acvnet-plus-kitti15	portable	14.3 ms / 136 / 208	38.0 ms / 401 / 664	99.4 ms / 887 / 1418	623.7 ms / 5054 / 8076
fast-acvnet-plus-kitti15	kernels	7.8 ms / 169 / 546	25.3 ms / 457 / 598	66.2 ms / 1006 / 1226	422.8 ms / 5743 / 7342
fast-acvnet-plus-sceneflow	portable	14.3 ms / 136 / 208	38.0 ms / 401 / 664	99.4 ms / 887 / 1418	623.7 ms / 5054 / 8076
fast-acvnet-plus-sceneflow	kernels	7.8 ms / 169 / 546	25.3 ms / 457 / 598	66.1 ms / 1006 / 1226	422.7 ms / 5743 / 7342
fast-acvnet-sceneflow	portable	14.0 ms / 137 / 208	31.1 ms / 402 / 664	78.2 ms / 890 / 1418	492.4 ms / 5077 / 8074
fast-acvnet-sceneflow	kernels	6.8 ms / 169 / 582	22.0 ms / 435 / 608	55.6 ms / 954 / 1372	357.6 ms / 5445 / 7618

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

• fast-acvnet-kitti12, fast-acvnet-kitti15, fast-acvnet-sceneflow: same network and settings as fast-acvnet-generalization, measured once (rows not repeated).
• fast-acvnet-plus-kitti12, fast-acvnet-plus-kitti15, fast-acvnet-plus-sceneflow: same network and settings as fast-acvnet-plus-generalization, 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 fp32	original fp16	portable	kernels	note
fast-acvnet-generalization	ETH3D two-view train, bad > 1 px % / EPE	6.50 / 0.775	6.51 / 0.778	6.50 / 0.776	6.51 / 0.776
fast-acvnet-generalization	Middlebury 2014 trainingH, bad > 2 px % / EPE	18.57 / 4.203	18.62 / 4.216	18.61 / 4.224	18.60 / 4.220
fast-acvnet-sceneflow	ETH3D two-view train, bad > 1 px % / EPE	8.40 / 0.654	8.44 / 0.659	8.40 / 0.657	8.43 / 0.657
fast-acvnet-sceneflow	Middlebury 2014 trainingH, bad > 2 px % / EPE	25.53 / 7.667	25.55 / 7.686	25.53 / 7.676	25.55 / 7.690
fast-acvnet-kitti12	ETH3D two-view train, bad > 1 px % / EPE	19.07 / 0.825	19.06 / 0.825	19.06 / 0.826	19.06 / 0.825
fast-acvnet-kitti12	Middlebury 2014 trainingH, bad > 2 px % / EPE	28.20 / 6.892	28.23 / 6.888	28.21 / 6.880	28.21 / 6.886
fast-acvnet-kitti15	ETH3D two-view train, bad > 1 px % / EPE	17.58 / 0.774	17.58 / 0.772	17.61 / 0.772	17.59 / 0.772
fast-acvnet-kitti15	Middlebury 2014 trainingH, bad > 2 px % / EPE	28.67 / 6.920	28.71 / 6.918	28.67 / 6.918	28.69 / 6.928
fast-acvnet-plus-generalization	ETH3D two-view train, bad > 1 px % / EPE	6.19 / 0.638	6.19 / 0.640	6.23 / 0.640	6.20 / 0.637
fast-acvnet-plus-generalization	Middlebury 2014 trainingH, bad > 2 px % / EPE	18.68 / 4.225	18.70 / 4.227	18.69 / 4.229	18.69 / 4.229
fast-acvnet-plus-sceneflow	ETH3D two-view train, bad > 1 px % / EPE	12.64 / 3.063	12.61 / 3.061	12.61 / 3.059	12.62 / 3.065
fast-acvnet-plus-sceneflow	Middlebury 2014 trainingH, bad > 2 px % / EPE	33.29 / 9.716	33.28 / 9.701	33.27 / 9.689	33.29 / 9.705
fast-acvnet-plus-kitti12	ETH3D two-view train, bad > 1 px % / EPE	21.63 / 0.821	21.64 / 0.821	21.61 / 0.821	21.62 / 0.821
fast-acvnet-plus-kitti12	Middlebury 2014 trainingH, bad > 2 px % / EPE	29.87 / 7.098	29.88 / 7.104	29.87 / 7.103	29.87 / 7.105
fast-acvnet-plus-kitti15	ETH3D two-view train, bad > 1 px % / EPE	21.62 / 0.822	21.63 / 0.822	21.61 / 0.822	21.63 / 0.822
fast-acvnet-plus-kitti15	Middlebury 2014 trainingH, bad > 2 px % / EPE	28.09 / 6.656	28.10 / 6.664	28.09 / 6.663	28.09 / 6.662

Measured on portable: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-05; kernels: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-06.

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.

Usage

from fastforks import stereo

model = stereo.load("fast-acvnet-generalization")  # 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: fast-acvnet-generalization, fast-acvnet-kitti12, fast-acvnet-kitti15, fast-acvnet-plus-generalization, fast-acvnet-plus-kitti12, fast-acvnet-plus-kitti15, fast-acvnet-plus-sceneflow, fast-acvnet-sceneflow.

Kernels backend

stereo.load("fast-acvnet-generalization", 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.