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Miayan/stereo-matching-weights
stereo-matching-weights is a depth estimation model from Miayan. Use it for the depth estimation task on the model card, and read the license before you ship it in a product. The card lists the license as other.
Mirror of three stereo-matching model checkpoints, re-hosted on Hugging Face so they can be pulled from behind a corporate firewall that blocks Google Drive. All weights are copied verbatim from their original public…
Downloads · 30 days
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Updated Aug 9, 2026
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.pth930 MB · 52%
From the Hugging Face model README
Mirror of three stereo-matching model checkpoints, re-hosted on Hugging Face so they can be pulled from behind a corporate firewall that blocks Google Drive. All weights are copied verbatim from their original public sources — credit and licensing belong to the original authors.
foundationstereo/ — Fast-FoundationStereoSource: Google Drive folder 1HuTt7UIp7gQsMiDvJwVuWmKpvFzIIMap.
Each timestamped folder is one complete model (model_best_bp2_serialize.pth + cfg.yaml):
| Folder | Notes |
|---|---|
23-36-37 | Most accurate (PyTorch 49.4 ms / TRT 23.4 ms) |
20-26-39 | Middle |
20-30-48 | Fastest (PyTorch 38.4 ms / TRT 16.6 ms) |
15-44-51 | Extra model included in the source folder |
onnx/ holds pre-exported ONNX models for 20_26_39, 20_30_48, 23_36_37
at 320x736 / 576x960 and 4 / 8 iterations.
liteanystereo/ — LiteAnyStereoSource: Google Drive folder 1UvDx296pVk7pC2rozKIpQF_EXcOleZOB.
LAS1/ — LAS1 checkpoints, incl. LiteAnyStereo.pth (not available on HF elsewhere).LAS2/ — LAS2 S/M/H/L (same as tomtomtommi/LiteAnyStereoV2, included for completeness).mobilestereonet/ — MobileStereoNetSource: individual Google Drive file IDs (see MobileStereoNet repo).
2D and 3D variants, trained on different combinations of SceneFlow (SF),
DrivingStereo (DS) and KITTI2015. *DS+KITTI2015* and *SF+DS+KITTI2015*
are the best on KITTI2015 val.
# NOTE: on the corporate network, add these two lines BEFORE importing
# huggingface_hub, or the TLS interception makes SSL verification fail:
import truststore
truststore.inject_into_ssl()
from huggingface_hub import snapshot_download, hf_hub_download
# whole repo
snapshot_download(repo_id="Miayan/stereo-matching-weights", local_dir="./weights")
# single file
hf_hub_download(repo_id="Miayan/stereo-matching-weights",
filename="foundationstereo/23-36-37/model_best_bp2_serialize.pth",
local_dir="./weights")