MonSter (fastforks)

MonSter (Cheng et al. 2025): Depth Anything V2 (ViT-L) monocular branch + IGEV-style geometry volume, with stereo and mono ConvGRUs that refine each other, 32 iterations. The original README flags its monster-sceneflow checkpoint as buggy.

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/Junda24/MonSter at 49016c9a72b4.

Author weight repos: https://huggingface.co/cjd24/MonSter.

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
monster-mix-all@1	mix_all.pth	fe0f25eacc4d	LicenseRef-noncommercial AND CC-BY-NC-4.0	no
monster-sceneflow@1	sceneflow.pth	ece58ae6f8ff	LicenseRef-noncommercial AND CC-BY-NC-4.0	no

Licence

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

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

Licence evidence: https://github.com/Junda24/MonSter/blob/49016c9a72b4260c0d119c77bb14e78a132cea8c/README.md#L93

Input size

any H x W >= 32, padded to a multiple of 32 Tested up to (portable, fp16, RTX 5060 Ti 16 GB): monster-mix-all 1988x2880 (6.8 GiB peak allocated, 9.0 reserved); monster-sceneflow 1988x2880 (6.8 GiB peak allocated, 9.0 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
monster-mix-all	4275.0	4276.7	1585.15	667.63	6.4x	2.4x	6382 / 6430	2336 / 2494	2356 / 2714

Startup and first call

model	load (s): original	portable	kernels	first call (s): original	portable	kernels
monster-mix-all	5.85 (375x1242)	4.83	0.95†	1.26 (375x1242)	1.59†	0.67†

Fixed-size speed and peak memory, all models

model	mode	640x480	1280x720	1920x1080	12 MP 4000x3000
monster-mix-all	portable	284.1 ms / 1089 / 1184	894.5 ms / 1732 / 2154	2291.3 ms (n=3) / 2935 / 3668	OOM
monster-mix-all	kernels	127.2 ms / 1112 / 1250	379.5 ms / 1752 / 2288	956.8 ms / 2939 / 3460	OOM
monster-sceneflow	portable	284.1 ms / 1089 / 1184	894.5 ms / 1732 / 2154	2290.9 ms (n=3) / 2935 / 3668	OOM
monster-sceneflow	kernels	127.1 ms / 1112 / 1250	379.4 ms / 1752 / 2288	956.9 ms / 2939 / 3460	OOM

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

• monster-sceneflow: same network and settings as monster-mix-all, 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("monster-mix-all")  # 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: monster-mix-all, monster-sceneflow.

Kernels backend

stereo.load("monster-mix-all", mode="kernels") runs our CUDA kernels (igev, cutlass_conv, cudnn_hacc, fastbb). 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.