thanks to naver ❤
Browse files- MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth +3 -0
- README.md +57 -0
- config.json +34 -0
- model.safetensors +3 -0
MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:e28f91b488554653e2b46ddae9c78c1143e0bcb2e27d3e26cdb0b717f1568eb2
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README.md
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---
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tags:
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- image-to-3d
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- pytorch_model_hub_mixin
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- model_hub_mixin
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library_name: mast3r
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repo_url: https://github.com/naver/mast3r
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---
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## Grounding Image Matching in 3D with MASt3R
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```bibtex
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@misc{mast3r_arxiv24,
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title={Grounding Image Matching in 3D with MASt3R},
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author={Vincent Leroy and Yohann Cabon and Jerome Revaud},
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year={2024},
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eprint={2406.09756},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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@inproceedings{dust3r_cvpr24,
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title={DUSt3R: Geometric 3D Vision Made Easy},
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author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
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booktitle = {CVPR},
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year = {2024}
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}
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```
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# License
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The code is distributed under the CC BY-NC-SA 4.0 License. See [LICENSE](https://github.com/naver/mast3r/blob/main/LICENSE) for more information.
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For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0.
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The mapfree dataset license in particular is very restrictive. For more information, check [CHECKPOINTS_NOTICE](https://github.com/naver/mast3r/blob/main/CHECKPOINTS_NOTICE).
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# Model info
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Gihub page: https://github.com/naver/mast3r/
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| Modelname | Training resolutions | Head | Encoder | Decoder |
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|-------------|----------------------|------|---------|---------|
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| MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric | 512x384, 512x336, 512x288, 512x256, 512x160 | CatMLP+DPT | ViT-L | ViT-B |
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# How to use
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First, [install mast3r](https://github.com/naver/mast3r?tab=readme-ov-file#installation).
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To load the model:
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```python
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from mast3r.model import AsymmetricMASt3R
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import torch
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model = AsymmetricMASt3R.from_pretrained("naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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```
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config.json
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{
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"conf_mode": [
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"exp",
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1,
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Infinity
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],
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"dec_depth": 12,
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"dec_embed_dim": 768,
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"dec_num_heads": 12,
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"depth_mode": [
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"exp",
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-Infinity,
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Infinity
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],
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"desc_conf_mode": [
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"exp",
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0,
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Infinity
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],
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"desc_mode": "norm",
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"enc_depth": 24,
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"enc_embed_dim": 1024,
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"enc_num_heads": 16,
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"head_type": "catmlp+dpt",
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"img_size": [
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512,
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512
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],
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"landscape_only": false,
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"output_mode": "pts3d+desc24",
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"patch_embed_cls": "PatchEmbedDust3R",
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"pos_embed": "RoPE100",
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"two_confs": true
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0a615eb05fa9db654050aa655945ee5696e7c6c1b7f93f1ee8c37249010f6feb
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size 2754661648
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