fastforks/monster-plusplus

MonSter++ (fastforks)

MonSter++ (Cheng et al. 2025), accuracy model (the MonSter network, max disparity 416): Depth Anything V2 (ViT-L) monocular branch + IGEV-style geometry volume, with stereo and mono ConvGRUs that refine each other, 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/Junda24/MonSter-plusplus at 9167101af8fb.

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

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-plusplus-mix-all@1	Mix_all_large.pth	e86dede921dc	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: mix_all: 'all the datasets we collect over 2M image pairs' (README), not listed.

Licence evidence: https://github.com/Junda24/MonSter-plusplus/blob/9167101af8fbe93c706707daa7a8181089c8b1b4/LICENSE#L34

Input size

any H x W >= 32, padded to a multiple of 32 Tested up to (portable, fp16, RTX 5060 Ti 16 GB): monster-plusplus-mix-all 1988x2880 (7.7 GiB peak allocated, 9.6 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-plusplus-mix-all	4339.1	4334.5	1632.62	697.92	6.2x	2.3x	6382 / 6430	2583 / 2918	2438 / 2760

Startup and first call

model	load (s): original	portable	kernels	first call (s): original	portable	kernels
monster-plusplus-mix-all	8.37 (375x1242)	2.45	0.79†	1.23 (375x1242)	1.63†	0.70†

Fixed-size speed and peak memory, all models

model	mode	640x480	1280x720	1920x1080	12 MP 4000x3000
monster-plusplus-mix-all	portable	294.3 ms / 1138 / 1246	928.8 ms / 1880 / 2318	2370.0 ms (n=3) / 3269 / 3812	OOM
monster-plusplus-mix-all	kernels	132.9 ms / 1130 / 1270	396.3 ms / 1804 / 2344	998.1 ms / 3051 / 3724	OOM

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

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| <= 0.2 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.03 px and p99 <= 0.25 px.

Usage

from fastforks import stereo

model = stereo.load("monster-plusplus-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-plusplus-mix-all.

Kernels backend

stereo.load("monster-plusplus-mix-all", mode="kernels") runs our CUDA kernels (igev, igevpp, 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.