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README.md
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### Checkpoints
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You can obtain the checkpoints by two ways:
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1) You can use our huggingface_hub integration: the models will be downloaded automatically.
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For these checkpoints, make sure to agree to the license of all the training datasets 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](CHECKPOINTS_NOTICE).
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### Interactive demo
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python3 demo.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric
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# Use --weights to load a checkpoint from a local file, eg --weights checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth
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# Use --retrieval_model and point to the retrieval checkpoint to enable retrieval as a pairing strategy, asmk must be installed
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# Use --local_network to make it accessible on the local network, or --server_name to specify the url manually
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# Use --server_port to change the port, by default it will search for an available port starting at 7860
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# Use --device to use a different device, by default it's "cuda"
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### Checkpoints
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#### MASt3R Model
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You can obtain the model checkpoints by two ways:
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1) You can use our huggingface_hub integration: the models will be downloaded automatically.
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For these checkpoints, make sure to agree to the license of all the training datasets 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](CHECKPOINTS_NOTICE).
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#### Retrieval Model
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You need to download both the `trainingfree.pth` and `codebook.pkl` files, and put them in the same directory.
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For `MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric`:
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[`MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree`](https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth)
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[`MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_codebook`](https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_codebook.pkl)
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```bash
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mkdir -p checkpoints/
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wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth -P checkpoints/
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wget https://download.europe.naverlabs.com/ComputerVision/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_codebook.pkl -P checkpoints/
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```
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### Interactive demo
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python3 demo.py --model_name MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric
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# Use --weights to load a checkpoint from a local file, eg --weights checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth
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# Use --retrieval_model and point to the retrieval checkpoint (*trainingfree.pth) to enable retrieval as a pairing strategy, asmk must be installed
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# Use --local_network to make it accessible on the local network, or --server_name to specify the url manually
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# Use --server_port to change the port, by default it will search for an available port starting at 7860
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# Use --device to use a different device, by default it's "cuda"
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