| --- |
| library_name: "fastforks" |
| pipeline_tag: "depth-estimation" |
| license: "other" |
| license_name: "noncommercial" |
| license_link: "https://github.com/Junda24/MonSter-plusplus/blob/9167101af8fbe93c706707daa7a8181089c8b1b4/LICENSE#L34" |
| tags: |
| - "stereo" |
| - "stereo-matching" |
| - "disparity" |
| - "cuda" |
| - "pytorch" |
| - "fastforks" |
| --- |
| # 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`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/stereo/models/rt_monster_plusplus/card.toml). |
|
|
| | variant | weights (author link) | SHA-256 | weights licence | commercial use | |
| |---|---|---|---|---| |
| | `rt-monster-plusplus-mix-all@1` | [Zero_shot.pth](https://huggingface.co/cjd24/MonSter-plusplus/resolve/main/Zero_shot.pth?download=true) | `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` | 251.9 | 251.4 | 113.4 | 94.13 | 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 | 113.4 | 77.0 | 69.4 | fails: TensorRT engine build failed | – | 94.13 | 2592 / 2646 | 2750 / 2942 | – | |
|
|
| ### Fixed-size speed and peak memory, all models |
|
|
| | model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 | |
| | --- | --- | --- | --- | --- | --- | |
| | `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](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 |
| |
| ### 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 |
| |
| ```python |
| 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. |
| |
| <!-- staged from fastforks 7204e33f (2026-11-05) --> |
| |
