monster / README.md

fastforks 0.1.0

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---
library_name: "fastforks"
pipeline_tag: "depth-estimation"
license: "other"
license_name: "noncommercial-and-cc-by-nc-4.0"
license_link: "https://github.com/Junda24/MonSter/blob/49016c9a72b4260c0d119c77bb14e78a132cea8c/README.md#L93"
tags:
- "stereo"
- "stereo-matching"
- "disparity"
- "cuda"
- "pytorch"
- "fastforks"
---
# 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`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/stereo/models/monster/card.toml).

| variant | weights (author link) | SHA-256 | weights licence | commercial use |
|---|---|---|---|---|
| `monster-mix-all@1` | [mix_all.pth](https://huggingface.co/cjd24/MonSter/resolve/e6821d996174f60ea0cb05d14eaaa37b34777f6e/mix_all.pth?download=true) | `fe0f25eacc4d` | LicenseRef-noncommercial AND CC-BY-NC-4.0 | **no** |
| `monster-sceneflow@1` | [sceneflow.pth](https://huggingface.co/cjd24/MonSter/resolve/e6821d996174f60ea0cb05d14eaaa37b34777f6e/sceneflow.pth?download=true) | `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 | 4277 | 1585 | 668 | 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† |

### 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-sceneflow` | 994x1482 | 1585 | 1096.3 | 102.6 | 671.0 | 384 | 668 | 2336 / 2494 | 2081 / 2272 | 2018 |

### Fixed-size speed and peak memory, all models

| model | mode | 480x640 | 720x1280 | 1080x1920 | 12 MP 3000x4000 |
| --- | --- | --- | --- | --- | --- |
| `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 |

### Apple silicon (MPS)

| model | `portable` | `kernels` | speed-up |
| --- | --- | --- | --- |
| `monster-sceneflow` | 3344 ms / 3491 MiB (float16) | 2756 ms / 4093 MiB | 1.21x |

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](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
### On public datasets
Original = the authors' code, same checkpoint and protocol.
| variant | dataset | original fp32 | original fp16 | portable | kernels | note |
|---|---|---|---|---|---|---|
| `monster-sceneflow` | ETH3D two-view train, bad > 1 px % / EPE | 1.34 / 0.222 | 1.33 / 0.221 | 1.34 / 0.223 | 1.34 / 0.220 |  |
| `monster-sceneflow` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 10.20 / 1.922 | 10.20 / 1.922 | 10.18 / 1.921 | 10.16 / 1.920 |  |
| `monster-mix-all` | ETH3D two-view train, bad > 1 px % / EPE | 1.25 / 0.186 | 1.26 / 0.186 | 1.24 / 0.186 | 1.25 / 0.186 | trained on this dataset's public images; not a zero-shot result |
| `monster-mix-all` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 7.82 / 2.402 | 7.82 / 2.398 | 7.85 / 2.395 | 7.84 / 2.402 | trained on this dataset's public images; not a zero-shot result |
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| <= 0.002 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("monster-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-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.
<!-- staged from fastforks feccac41 (2026-11-06) -->

	---
	library_name: "fastforks"
	pipeline_tag: "depth-estimation"
	license: "other"
	license_name: "noncommercial-and-cc-by-nc-4.0"
	license_link: "https://github.com/Junda24/MonSter/blob/49016c9a72b4260c0d119c77bb14e78a132cea8c/README.md#L93"
	tags:
	- "stereo"
	- "stereo-matching"
	- "disparity"
	- "cuda"
	- "pytorch"
	- "fastforks"
	---
	# 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`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/stereo/models/monster/card.toml).

	\| variant \| weights (author link) \| SHA-256 \| weights licence \| commercial use \|
	\|---\|---\|---\|---\|---\|
	\| `monster-mix-all@1` \| [mix_all.pth](https://huggingface.co/cjd24/MonSter/resolve/e6821d996174f60ea0cb05d14eaaa37b34777f6e/mix_all.pth?download=true) \| `fe0f25eacc4d` \| LicenseRef-noncommercial AND CC-BY-NC-4.0 \| no \|
	\| `monster-sceneflow@1` \| [sceneflow.pth](https://huggingface.co/cjd24/MonSter/resolve/e6821d996174f60ea0cb05d14eaaa37b34777f6e/sceneflow.pth?download=true) \| `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 \| 4277 \| 1585 \| 668 \| 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† \|

	### 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-sceneflow` \| 994x1482 \| 1585 \| 1096.3 \| 102.6 \| 671.0 \| 384 \| 668 \| 2336 / 2494 \| 2081 / 2272 \| 2018 \|

	### Fixed-size speed and peak memory, all models

	\| model \| mode \| 480x640 \| 720x1280 \| 1080x1920 \| 12 MP 3000x4000 \|
	\| --- \| --- \| --- \| --- \| --- \| --- \|
	\| `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 \|

	### Apple silicon (MPS)

	\| model \| `portable` \| `kernels` \| speed-up \|
	\| --- \| --- \| --- \| --- \|
	\| `monster-sceneflow` \| 3344 ms / 3491 MiB (float16) \| 2756 ms / 4093 MiB \| 1.21x \|

	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](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

	### On public datasets

	Original = the authors' code, same checkpoint and protocol.

	\| variant \| dataset \| original fp32 \| original fp16 \| portable \| kernels \| note \|
	\|---\|---\|---\|---\|---\|---\|---\|
	\| `monster-sceneflow` \| ETH3D two-view train, bad > 1 px % / EPE \| 1.34 / 0.222 \| 1.33 / 0.221 \| 1.34 / 0.223 \| 1.34 / 0.220 \| \|
	\| `monster-sceneflow` \| Middlebury 2014 trainingH, bad > 2 px % / EPE \| 10.20 / 1.922 \| 10.20 / 1.922 \| 10.18 / 1.921 \| 10.16 / 1.920 \| \|
	\| `monster-mix-all` \| ETH3D two-view train, bad > 1 px % / EPE \| 1.25 / 0.186 \| 1.26 / 0.186 \| 1.24 / 0.186 \| 1.25 / 0.186 \| trained on this dataset's public images; not a zero-shot result \|
	\| `monster-mix-all` \| Middlebury 2014 trainingH, bad > 2 px % / EPE \| 7.82 / 2.402 \| 7.82 / 2.398 \| 7.85 / 2.395 \| 7.84 / 2.402 \| trained on this dataset's public images; not a zero-shot result \|

	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\| <= 0.002 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("monster-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-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.

	<!-- staged from fastforks feccac41 (2026-11-06) -->