|
--- |
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task_categories: |
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- image-classification |
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size_categories: |
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- 1K<n<10K |
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viewer: false |
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license: cc-by-nc-4.0 |
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--- |
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|
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## Dataset Description |
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We introduce a challenging dataset for identifying machine parts from real photos, |
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featuring images of 102 parts from a labeling machine. This dataset was developed |
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with the complexity of real-world scenarios in mind and highlights the complexity |
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of distinguishing between closely related classes, providing an opportunity to |
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improve domain adaption methods. The dataset includes 3,264 CAD-rendered |
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images (32 per part) and 6,146 real images (6 to 137 per part) for UDA and |
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testing. Rendered images were produced using a Blender-based pipeline with |
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environment maps, lights, and virtual cameras arranged to ensure varied mesh |
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orientations. We also use material metadata and apply one of 21 texture materials |
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to the objects. We render all images at 512x512 pixels. The real photo set consists of |
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raw images captured under varying conditions using different cameras, including |
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varied lighting, backgrounds, and environmental factors. |
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|
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Update: |
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* Fix material issues for some objects. (real was black steel but synth was natural steel) |
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* Add train & test estimated depth data from ZoeDepth |
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* Add unprocessed (uncropped) test image data with bounding box labels |
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* Add depth data exported from render pipeline (blender) via compositing graph. (raw EXR & normalized PNG) |
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* Add training images including ControlNet generated wood backgrounds |
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* Add training images including ControlNet generted hands |
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* Add training images processed by T2i-Adapter Style Transfer |
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|
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## Download |
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[Download zipped dataset](https://huggingface.co/datasets/ritterdennis/topex-printer/resolve/main/topex-printer.zip) |
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## Licensing Information |
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[CC BY-NC 4.0 Deed](https://creativecommons.org/licenses/by-nc/4.0/deed.en) |
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|
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### Citation Information |
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Please cite our work when using the dataset. |
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``` |
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@misc{ritter2023cad, |
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title={CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification}, |
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author={Dennis Ritter and Mike Hemberger and Marc Hönig and Volker Stopp and Erik Rodner and Kristian Hildebrand}, |
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year={2023}, |
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eprint={2310.04757}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
