--- library_name: "fastforks" pipeline_tag: "depth-estimation" license: "other" license_name: "mit-code-weights-unstated" license_link: "https://github.com/isl-org/MiDaS/blob/454597711a62eabcbf7d1e89f3fb9f569051ac9b/LICENSE" tags: - "depth" - "mono-depth" - "monocular-depth" - "cuda" - "pytorch" - "fastforks" --- # MiDaS v3.1 (fastforks) MiDaS v3.1 (Birkl et al. 2023): relative monocular depth from a BEiT-L/16 encoder and a DPT decoder, trained on a mix of 12 depth datasets. 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: . Kernels for the `kernels` backend: . Original code: at `4545977`. Task: depth. Tier: experimental. Outputs: `inverse_depth`. Backends: `portable`, `kernels`. Input: one RGB image. Output: a depth or inverse-depth map. `kind` (a card field, `result.kind`) says what the numbers mean: `metric` = metres; `scale_invariant`, `affine_invariant` and `affine_invariant_inverse` are relative (unknown scale or shift, not metres). Metric models that use the focal length take `model(image, intrinsics=K)` (K `[3, 3]`, pixels of the input): `metric3d-vit-large` and `da3metric-large` need it for correct metres (`da3metric-large`: metres = output x focal / 300; without `intrinsics=` a 300 px focal length is assumed, wrong for a real camera). `unidepth-v2-vitl14`, `unik3d-vitl`, `depth-pro` and MoGe estimate the camera themselves (given `intrinsics=` replace `fx`); `zoedepth-nk` and the relative models (`da3mono-large` too) take none. ## 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/depth/models/midas/card.toml). | variant | weights (author link) | SHA-256 | weights licence | commercial use | |---|---|---|---|---| | `midas-dpt-beit-large-512@1` | [dpt_beit_large_512.pt](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt) | `9e9e900747e9` | MIT (assumed from code) | yes | ## Licence Original-code strings, recorded as they are (research-use package; check them before any other use). Code: MIT. Files: `LICENSE`. Code licence: MIT; the authors do not state a separate weights licence. Training data: 12 datasets (ReDWeb, DIML, Movies, MegaDepth, WSVD, TartanAir, HRWSI, ApolloScape, BlendedMVS, IRS, KITTI, NYU Depth V2) per the MiDaS v3.1 paper (arXiv 2307.14460). Licence evidence: https://github.com/isl-org/MiDaS/blob/454597711a62eabcbf7d1e89f3fb9f569051ac9b/LICENSE (weights are release assets of the same repository) ## Input size any H x W >= 8, resized or padded inside the model Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `midas-dpt-beit-large-512` 3000x4000 (1.8 GiB peak allocated, 1.9 reserved) ## Speed and memory ### Depth, 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 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `midas-dpt-beit-large-512` | 385.6 | 333.2 | 78.79 | 63.89 | 5.2x | 1.2x | 2759 / 2898 | 992 / 1418 | 1003 / 1436 | ### Startup and first call | model | load (s): original | portable | kernels | first call (s): original | portable | kernels | | --- | --- | --- | --- | --- | --- | --- | | `midas-dpt-beit-large-512` | 5.16 | 2.49 | 0.28† | 1.14 | 0.09 | 0.07 | ### 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) | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `midas-dpt-beit-large-512` | 994x1482 | 78.79 | fails: Dynamo: inference_mode in model code | – | 52.2 | 54 | 63.89 | 992 / 1418 | – | 800 | ### Fixed-size speed and peak memory, all models | model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 | | --- | --- | --- | --- | --- | --- | | `midas-dpt-beit-large-512` | portable | 79.3 ms / 1729 / 1880 | 141.1 ms / 1870 / 2052 | 141.2 ms / 1883 / 2080 | 81.6 ms / 1863 / 1990 | | `midas-dpt-beit-large-512` | kernels | 52.1 ms / 922 / 1048 | 96.0 ms / 1012 / 1220 | 96.0 ms / 1025 / 1280 | 53.1 ms / 1077 / 1178 | Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB). Resizes to its working resolution: time depends on the aspect ratio, not the input pixels. 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 AbsRel / delta1 after a least-squares scale + shift fit (in disparity space for inverse-depth models), mean over images. Original = the authors' code, same checkpoint and protocol. | variant | dataset | original | portable | kernels | note | |---|---|---|---|---|---| | `midas-dpt-beit-large-512` | NYUv2 test, AbsRel / delta1 | 0.0487 / 0.9789 | 0.0487 / 0.9789 | 0.0487 / 0.9789 | | | `midas-dpt-beit-large-512` | iBims-1, AbsRel / delta1 | 0.0477 / 0.9727 | 0.0478 / 0.9726 | 0.0478 / 0.9726 | | | `midas-dpt-beit-large-512` | ETH3D two-view train left, AbsRel / delta1 | 0.0904 / 0.8978 | 0.0906 / 0.8977 | 0.0908 / 0.8972 | possibly in the training data | | `midas-dpt-beit-large-512` | Middlebury 2014 trainingH left, AbsRel / delta1 | 0.0425 / 0.9906 | 0.0424 / 0.9906 | 0.0425 / 0.9906 | possibly in the training data | Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-10-27. ### Parity with the original code Every variant is checked against golden outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.02 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.5 % and p99 <= 2 % of the reference's median |value| (absolute error of the output `inverse_depth`). ## Usage ```python from fastforks import depth model = depth.load("midas-dpt-beit-large-512") # mode="auto": the preferred backend from recorded measurements result = model("room.jpg") result.inverse_depth # [H, W] float32 tensor; result.kind says what the maps mean # models that take a camera: model("room.jpg", intrinsics=K), K [3, 3] in pixels result.backend # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16') ``` Install with `pip install fastforks` (see ). Other variants: `midas-dpt-beit-large-512`. ## Kernels backend `depth.load("midas-dpt-beit-large-512", mode="kernels")` runs our CUDA kernels (`cudnn_hacc`). It needs a prebuilt kernel for your GPU architecture (downloaded from 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.