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 | 4334 | 1633 | 698 | 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† |
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) |
|---|---|---|---|---|---|---|---|---|---|---|
monster-plusplus-mix-all |
994x1482 | 1633 | 1126.0 | 108.9 | 697.0 | 416 | 698 | 2583 / 2918 | 2328 / 2532 | 2642 |
Fixed-size speed and peak memory, all models
| model | mode | 480x640 | 720x1280 | 1080x1920 | 12 MP 3000x4000 |
|---|---|---|---|---|---|
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 |
Apple silicon (MPS)
| model | portable |
kernels |
speed-up |
|---|---|---|---|
monster-plusplus-mix-all |
3518 ms / 3490 MiB (float16) | 2928 ms / 3492 MiB | 1.20x |
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
On public datasets
Original = the authors' code, same checkpoint and protocol.
| variant | dataset | original fp32 | original fp16 | portable | kernels | note |
|---|---|---|---|---|---|---|
monster-plusplus-mix-all |
ETH3D two-view train, bad > 1 px % / EPE | 1.08 / 0.175 | 1.08 / 0.175 | 1.08 / 0.175 | 1.08 / 0.175 | possibly in the training data |
monster-plusplus-mix-all |
Middlebury 2014 trainingH, bad > 2 px % / EPE | 3.77 / 0.630 | 3.78 / 0.630 | 3.75 / 0.630 | 3.75 / 0.630 | possibly in the training data |
Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-05.
Parity with the original code
Every variant is checked against reference outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.2 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
# non-commercial weights: run with FASTFORKS_NONCOMMERCIAL=1 in the environment
model = stereo.load("monster-plusplus-mix-all") # 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: 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.