--- library_name: "fastforks" pipeline_tag: "depth-estimation" license: "other" license_name: "apache-2.0-code-weights-unstated" license_link: "https://github.com/google-research/google-research/blob/f200f2b89d29484bf0dd0fef0c3979ac8bed9af4/LICENSE" tags: - "stereo" - "stereo-matching" - "disparity" - "cuda" - "pytorch" - "fastforks" --- # HITNet (fastforks) HITNet (Tankovich et al. 2021): slanted tile hypotheses propagated and refined from coarse to fine; the authors' ETH3D (gray, max disparity 128) and Middlebury (half resolution, max disparity 400) graphs. 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 `f200f2b89d29`. 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/hitnet/card.toml). | variant | weights (author link) | SHA-256 | weights licence | commercial use | |---|---|---|---|---| | `hitnet-eth3d@1` | [eth3d.pb](https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/142_HITNET/resources.tar.gz) | `ab34a01de126` | Apache-2.0 (assumed from code) | yes | | `hitnet-middlebury-d400@1` | [middlebury_d400.pb](https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/142_HITNET/resources.tar.gz) | `cc386fbb3181` | 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: not stated by the authors. - Licence note: The authors state no weights licence; the checkpoints are fetched from the PINTO model-zoo mirror (a third-party host), not from Google, and the code licence is assumed to cover them. Licence evidence: ## Input size any H x W >= 8, resized or padded inside the model Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `hitnet-eth3d` 3000x4000 (6.4 GiB peak allocated, 10.8 reserved); `hitnet-middlebury-d400` 1988x2880 (6.4 GiB peak allocated, 10.1 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 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `hitnet-eth3d` | – | – | 67.78 | 23.16 | – | 2.9x | – | 860 / 1090 | 647 / 1248 | | `hitnet-middlebury-d400` | – | – | 176.0 | 52.73 | – | 3.3x | – | 1744 / 2414 | 1263 / 2368 | ### Startup and first call | model | load (s): original | portable | kernels | first call (s): original | portable | kernels | | --- | --- | --- | --- | --- | --- | --- | | `hitnet-eth3d` | – | 0.09 | 0.07† | – | 0.08† | 0.03† | | `hitnet-middlebury-d400` | – | 0.13 | 0.10† | – | 0.18† | 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) | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `hitnet-eth3d` | 994x1482 | 67.78 | 27.7 | 39.1 | 31.1 | 136 | 23.16 | 860 / 1090 | 719 / 728 | 836 | ### Fixed-size speed and peak memory, all models | model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 | | --- | --- | --- | --- | --- | --- | | `hitnet-eth3d` | portable | 18.1 ms / 221 / 336 | 40.2 ms / 659 / 1004 | 92.6 ms / 1134 / 2438 | 579.4 ms / 6558 / 11110 | | `hitnet-eth3d` | kernels | 5.2 ms / 140 / 470 | 14.5 ms / 392 / 482 | 30.8 ms / 836 / 1004 | 181.7 ms / 4814 / 5758 | | `hitnet-middlebury-d400` | portable | 35.8 ms / 602 / 838 | 108.8 ms / 1067 / 2222 | 251.7 ms / 2392 / 3814 | OOM | | `hitnet-middlebury-d400` | kernels | 11.7 ms / 266 / 966 | 33.1 ms / 759 / 978 | 74.2 ms / 1695 / 2248 | 426.2 ms / 9604 / 12664 | ### Apple silicon (MPS) | model | `portable` | `kernels` | speed-up | | --- | --- | --- | --- | | `hitnet-eth3d` | 206 ms / 2444 MiB (float32) | 74 ms / 1409 MiB | 2.80x | | `hitnet-middlebury-d400` | 448 ms / 4539 MiB (float32) | 175 ms / 1454 MiB | 2.57x | 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 | portable | kernels | note | |---|---|---|---|---| | `hitnet-eth3d` | ETH3D two-view train, bad > 1 px % / EPE | 0.93 / 0.164 | 0.93 / 0.164 | trained on this dataset's public images; not a zero-shot result | | `hitnet-eth3d` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 17.25 / 8.340 | 17.30 / 8.356 | | | `hitnet-middlebury-d400` | ETH3D two-view train, bad > 1 px % / EPE | 7.01 / 0.899 | 6.98 / 0.872 | | | `hitnet-middlebury-d400` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 7.54 / 1.187 | 7.52 / 1.184 | trained on this dataset's public images; not a zero-shot result | 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| <= 0.02 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.15 px and p99 <= 1.0 px. ## Limitations - CUDA output (float16, kernels, float32 with PyTorch's default TF32 convolutions) differs from the CPU by ~0.2 px mean / 4 px p99 at 720x1280: the argmin flips on near-equal tile costs. Full float32 (`torch.backends.cudnn.allow_tf32 = False`) matches the CPU to 0.00002 px. - The weights are the authors' frozen TensorFlow graphs read without TensorFlow; the FlyingThings and Middlebury d160 / d288 graphs are not included. - The original code is TensorFlow and was not timed. ## Usage ```python from fastforks import stereo model = stereo.load("hitnet-eth3d") # 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: `hitnet-eth3d`, `hitnet-middlebury-d400`. ## Kernels backend `stereo.load("hitnet-eth3d", mode="kernels")` runs our CUDA kernels (`hitnet`, `hitnet_conv`). 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.