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- README.md +6 -2
- {t3_large → models/t3_large}/decoders/cls_calandra17.pth +0 -0
- {t3_large → models/t3_large}/decoders/cls_cnc.pth +0 -0
- {t3_large → models/t3_large}/decoders/cls_objectfolder.pth +0 -0
- {t3_large → models/t3_large}/decoders/cls_touch_and_go.pth +0 -0
- {t3_large → models/t3_large}/decoders/cls_yuan18_fuzziness.pth +0 -0
- {t3_large → models/t3_large}/decoders/cls_yuan18_smoothness.pth +0 -0
- {t3_large → models/t3_large}/decoders/cls_yuan18_textile_type.pth +0 -0
- {t3_large → models/t3_large}/decoders/pose_estimation_3d.pth +0 -0
- {t3_large → models/t3_large}/decoders/pose_estimation_6d_digit.pth +0 -0
- {t3_large → models/t3_large}/decoders/pose_estimation_6d_mini.pth +0 -0
- {t3_large → models/t3_large}/decoders/pose_estimation_6d_wedge.pth +0 -0
- {t3_large → models/t3_large}/decoders/variance_estimation.pth +0 -0
- {t3_large → models/t3_large}/encoders/densetact.pth +0 -0
- {t3_large → models/t3_large}/encoders/finray.pth +0 -0
- {t3_large → models/t3_large}/encoders/mini.pth +0 -0
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- {t3_large → models/t3_large}/encoders/wedge.pth +0 -0
- {t3_large → models/t3_large}/trunk.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/cls_calandra17.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/cls_cnc.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/cls_objectfolder.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/cls_touch_and_go.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/cls_yuan18_fuzziness.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/cls_yuan18_smoothness.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/cls_yuan18_textile_type.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/pose_estimation_3d.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/pose_estimation_6d_digit.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/pose_estimation_6d_mini.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/pose_estimation_6d_wedge.pth +0 -0
- {t3_medium → models/t3_medium}/decoders/variance_estimation.pth +0 -0
- {t3_medium → models/t3_medium}/encoders/densetact.pth +0 -0
- {t3_medium → models/t3_medium}/encoders/finray.pth +0 -0
- {t3_medium → models/t3_medium}/encoders/mini.pth +0 -0
- {t3_medium → models/t3_medium}/encoders/svelte.pth +0 -0
- {t3_medium → models/t3_medium}/encoders/wedge.pth +0 -0
- {t3_medium → models/t3_medium}/trunk.pth +0 -0
- {t3_small → models/t3_small}/decoders/cls_calandra17.pth +0 -0
- {t3_small → models/t3_small}/decoders/cls_cnc.pth +0 -0
- {t3_small → models/t3_small}/decoders/cls_objectfolder.pth +0 -0
- {t3_small → models/t3_small}/decoders/cls_touch_and_go.pth +0 -0
- {t3_small → models/t3_small}/decoders/cls_yuan18_fuzziness.pth +0 -0
- {t3_small → models/t3_small}/decoders/cls_yuan18_smoothness.pth +0 -0
- {t3_small → models/t3_small}/decoders/cls_yuan18_textile_type.pth +0 -0
- {t3_small → models/t3_small}/decoders/pose_estimation_3d.pth +0 -0
- {t3_small → models/t3_small}/decoders/pose_estimation_6d_digit.pth +0 -0
- {t3_small → models/t3_small}/decoders/pose_estimation_6d_mini.pth +0 -0
- {t3_small → models/t3_small}/decoders/pose_estimation_6d_wedge.pth +0 -0
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README.md
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<svg class="btn-content" style="height: 1.5rem" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="github" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512" data-fa-i2svg=""><path fill="currentColor" d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg>
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<span class="btn-content">Code</span>
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<a href="
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<svg class="btn-content" style="height: 1.5rem" viewBox="0 0 24 24"><!--?lit$3829301306$--><g id="colab-logo"><path d="M4.54,9.46,2.19,7.1a6.93,6.93,0,0,0,0,9.79l2.36-2.36A3.59,3.59,0,0,1,4.54,9.46Z" style="fill:var(--colab-logo-dark)"></path><path d="M2.19,7.1,4.54,9.46a3.59,3.59,0,0,1,5.08,0l1.71-2.93h0l-.1-.08h0A6.93,6.93,0,0,0,2.19,7.1Z" style="fill:var(--colab-logo-light)"></path><path d="M11.34,17.46h0L9.62,14.54a3.59,3.59,0,0,1-5.08,0L2.19,16.9a6.93,6.93,0,0,0,9,.65l.11-.09" style="fill:var(--colab-logo-light)"></path>
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<path d="M12,7.1a6.93,6.93,0,0,0,0,9.79l2.36-2.36a3.59,3.59,0,1,1,5.08-5.08L21.81,7.1A6.93,6.93,0,0,0,12,7.1Z" style="fill:var(--colab-logo-light)"></path>
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<path d="M21.81,7.1,19.46,9.46a3.59,3.59,0,0,1-5.08,5.08L12,16.9A6.93,6.93,0,0,0,21.81,7.1Z" style="fill:var(--colab-logo-dark)"></path>
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Each `.tar` file is one sharded dataset. At runtime, wds (WebDataset) api automatically loads, shuffles, and unpacks all shards on demand.
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The nicest part of having a `.tar` file, instead of saving all raw data into matrices (e.g. `.npz` for zarr), is that `.tar` is easy to visualize without the need of any code.
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Simply double
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Although you will never need to unpack a `.tar` manually (wds does that automatically), it helps to understand the logic and file structure.
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@@ -77,6 +77,10 @@ For more details on operations of the paper, checkout our GitHub repository and
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<img src="https://t3.alanz.info/imgs/data_vis.jpg" width="80%" />
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## Citation
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MIT License.
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<svg class="btn-content" style="height: 1.5rem" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="github" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512" data-fa-i2svg=""><path fill="currentColor" d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg>
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<span class="btn-content">Code</span>
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<a href="https://colab.research.google.com/drive/1MmO9w1y59Gy6ds0iKlW04olszGko56Vf?usp=sharing" class="btn btn-light" role="button" aria-pressed="true">
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<svg class="btn-content" style="height: 1.5rem" viewBox="0 0 24 24"><!--?lit$3829301306$--><g id="colab-logo"><path d="M4.54,9.46,2.19,7.1a6.93,6.93,0,0,0,0,9.79l2.36-2.36A3.59,3.59,0,0,1,4.54,9.46Z" style="fill:var(--colab-logo-dark)"></path><path d="M2.19,7.1,4.54,9.46a3.59,3.59,0,0,1,5.08,0l1.71-2.93h0l-.1-.08h0A6.93,6.93,0,0,0,2.19,7.1Z" style="fill:var(--colab-logo-light)"></path><path d="M11.34,17.46h0L9.62,14.54a3.59,3.59,0,0,1-5.08,0L2.19,16.9a6.93,6.93,0,0,0,9,.65l.11-.09" style="fill:var(--colab-logo-light)"></path>
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<path d="M12,7.1a6.93,6.93,0,0,0,0,9.79l2.36-2.36a3.59,3.59,0,1,1,5.08-5.08L21.81,7.1A6.93,6.93,0,0,0,12,7.1Z" style="fill:var(--colab-logo-light)"></path>
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<path d="M21.81,7.1,19.46,9.46a3.59,3.59,0,0,1-5.08,5.08L12,16.9A6.93,6.93,0,0,0,21.81,7.1Z" style="fill:var(--colab-logo-dark)"></path>
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Each `.tar` file is one sharded dataset. At runtime, wds (WebDataset) api automatically loads, shuffles, and unpacks all shards on demand.
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The nicest part of having a `.tar` file, instead of saving all raw data into matrices (e.g. `.npz` for zarr), is that `.tar` is easy to visualize without the need of any code.
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Simply double click on any `.tar` file to check its content.
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Although you will never need to unpack a `.tar` manually (wds does that automatically), it helps to understand the logic and file structure.
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<img src="https://t3.alanz.info/imgs/data_vis.jpg" width="80%" />
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## Getting started
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Checkout our [Colab](https://colab.research.google.com/drive/1MmO9w1y59Gy6ds0iKlW04olszGko56Vf?usp=sharing) for a step-by-step tutorial!
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## Citation
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MIT License.
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