RT-MonSter++ (fastforks)
RT-MonSter++ (Cheng et al. 2025), the real-time MonSter++: Depth Anything V2 (ViT-S) monocular branch + coarse-to-fine (1/16, 1/8, 1/4) local geometry volumes, stereo and mono ConvGRUs, 4 updates.
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 |
|---|---|---|---|---|
rt-monster-plusplus-mix-all@1 |
Zero_shot.pth | 0741dd6f622b |
LicenseRef-noncommercial | 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 (Zero_shot.pth): '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): rt-monster-plusplus-mix-all 1988x2880 (9.3 GiB peak allocated, 11.1 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 |
|---|---|---|---|---|---|---|---|---|---|
rt-monster-plusplus-mix-all |
252 | 251 | 113 | 94.1 | 2.7x | 1.2x | 2922 / 3044 | 2592 / 2646 | 2704 / 3976 |
Startup and first call
| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
|---|---|---|---|---|---|---|
rt-monster-plusplus-mix-all |
0.99 (375x1242) | 0.26 | 0.23† | 0.66 (375x1242) | 0.13† | 0.10† |
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) |
|---|---|---|---|---|---|---|---|---|---|---|
rt-monster-plusplus-mix-all |
994x1482 | 113 | 77.0 | 69.4 | 44.1 | 150 | 94.1 | 2592 / 2646 | 2750 / 2942 | 952 |
Fixed-size speed and peak memory, all models
| model | mode | 480x640 | 720x1280 | 1080x1920 | 12 MP 3000x4000 |
|---|---|---|---|---|---|
rt-monster-plusplus-mix-all |
portable | 31.2 ms / 609 / 720 | 67.1 ms / 1625 / 1968 | 168.0 ms / 3478 / 4148 | OOM |
rt-monster-plusplus-mix-all |
kernels | 18.3 ms / 630 / 1934 | 57.5 ms / 1660 / 1998 | 140.1 ms / 3545 / 4188 | 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
On public datasets
Original = the authors' code, same checkpoint and protocol.
| variant | dataset | original fp32 | original fp16 | portable | kernels | note |
|---|---|---|---|---|---|---|
rt-monster-plusplus-mix-all |
ETH3D two-view train, bad > 1 px % / EPE | 2.05 / 0.226 | 2.05 / 0.226 | 2.05 / 0.226 | 2.05 / 0.226 | possibly in the training data |
rt-monster-plusplus-mix-all |
Middlebury 2014 trainingH, bad > 2 px % / EPE | 11.01 / 1.988 | 11.01 / 1.988 | 11.00 / 1.989 | 11.02 / 1.998 | possibly in the training data |
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
# non-commercial weights: run with FASTFORKS_NONCOMMERCIAL=1 in the environment
model = stereo.load("rt-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: rt-monster-plusplus-mix-all.
Kernels backend
stereo.load("rt-monster-plusplus-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.