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--- |
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license: cc-by-4.0 |
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task_categories: |
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- time-series-forecasting |
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tags: |
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- G-code, |
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- 3D_printing |
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- Time-Series |
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- Print_Duration |
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- Regression |
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- Extrinsic_Regression |
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- Multivariate |
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pretty_name: >- |
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3DTime: A Large Dataset of Multi-Annotated Multivariate Time-Series for |
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3D-printing Duration |
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size_categories: |
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- 10M<n<100M |
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--- |
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# 3DTime: A Large Dataset of Multi-Annotated Multivariate Time-Series for 3D-printing Duration |
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3DTime is a paper dataset currently under review at the AAAI-26 conference, for the main technical track. |
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We could not find a way to anonymously publish the full 1.2 TB dataset, hence this smaller version. |
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## Dataset content |
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The original dataset contains: |
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- 9,930 3D models, each sliced 4 times |
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- 39,720 annotated G-code files (compressed) |
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- 39,720 binary files |
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- A total of 12,442,224,222 G-code instructions |
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This smaller version contains: |
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- 100 3D models (~1% of the full dataset), each sliced 4 times |
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- 400 annotated G-code files (compressed) |
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- 400 binary files |
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- A total of 64,394,143 G-code instructions (~0.5% of the full dataset) |
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## Data loader for PyTorch |
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Due to the peculiar data format of this dataset (each G-code file has a specific length, some being over several million instructions long), we provide a custom made data loader designed for PyTorch utilization, instead of the standard Hugging Face automatic loader. |
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The code can be found in the complementary material of the submission. |
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This repository contains all of the code required for the dataset generation, statistic analysis, and model trainings that were presented in the paper. |
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