
## Dataset Description
We introduce a challenging dataset for identifying machine parts from real photos,
featuring images of 102 parts from a labeling machine. This dataset was developed
with the complexity of real-world scenarios in mind and highlights the complexity
of distinguishing between closely related classes, providing an opportunity to
improve domain adaption methods. The dataset includes 3,264 CAD-rendered
images (32 per part) and 6,146 real images (6 to 137 per part) for UDA and
testing. Rendered images were produced using a Blender-based pipeline with
environment maps, lights, and virtual cameras arranged to ensure varied mesh
orientations. We also use material metadata and apply one of 21 texture materials
to the objects. We render all images at 512x512 pixels. The real photo set consists of
raw images captured under varying conditions using different cameras, including
varied lighting, backgrounds, and environmental factors.

## Download
[Download zipped dataset](https://huggingface.co/datasets/ritterdennis/topex-printer/resolve/main/topex-printer.zip)

## Licensing Information
[CC BY-NC 4.0 Deed](https://creativecommons.org/licenses/by-nc/4.0/deed.en)

### Citation Information
Please cite our work when using the dataset.
```
@misc{ritter2023cad,
      title={CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification}, 
      author={Dennis Ritter and Mike Hemberger and Marc Hönig and Volker Stopp and Erik Rodner and Kristian Hildebrand},
      year={2023},
      eprint={2310.04757},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```
task_categories:image-classification size_categories:1K<n<10K license:cc-by-nc-4.0 arxiv:2310.04757 region:us
{"license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification"], "viewer": false}
TAGS
#task_categories-image-classification #size_categories-1K<n<10K #license-cc-by-nc-4.0 #arxiv-2310.04757 #region-us 


## Dataset Description
We introduce a challenging dataset for identifying machine parts from real photos,
featuring images of 102 parts from a labeling machine. This dataset was developed
with the complexity of real-world scenarios in mind and highlights the complexity
of distinguishing between closely related classes, providing an opportunity to
improve domain adaption methods. The dataset includes 3,264 CAD-rendered
images (32 per part) and 6,146 real images (6 to 137 per part) for UDA and
testing. Rendered images were produced using a Blender-based pipeline with
environment maps, lights, and virtual cameras arranged to ensure varied mesh
orientations. We also use material metadata and apply one of 21 texture materials
to the objects. We render all images at 512x512 pixels. The real photo set consists of
raw images captured under varying conditions using different cameras, including
varied lighting, backgrounds, and environmental factors.

## Download
Download zipped dataset

## Licensing Information
CC BY-NC 4.0 Deed


Please cite our work when using the dataset.

## Dataset Description
We introduce a challenging dataset for identifying machine parts from real photos,
featuring images of 102 parts from a labeling machine. This dataset was developed
with the complexity of real-world scenarios in mind and highlights the complexity
of distinguishing between closely related classes, providing an opportunity to
improve domain adaption methods. The dataset includes 3,264 CAD-rendered
images (32 per part) and 6,146 real images (6 to 137 per part) for UDA and
testing. Rendered images were produced using a Blender-based pipeline with
environment maps, lights, and virtual cameras arranged to ensure varied mesh
orientations. We also use material metadata and apply one of 21 texture materials
to the objects. We render all images at 512x512 pixels. The real photo set consists of
raw images captured under varying conditions using different cameras, including
varied lighting, backgrounds, and environmental factors. ## Download
Download zipped dataset ## Licensing Information
CC BY-NC 4.0 Deed


Please cite our work when using the dataset.
TAGS
#task_categories-image-classification #size_categories-1K<n<10K #license-cc-by-nc-4.0 #arxiv-2310.04757 #region-us 
 ## Dataset Description
We introduce a challenging dataset for identifying machine parts from real photos,
featuring images of 102 parts from a labeling machine. This dataset was developed
with the complexity of real-world scenarios in mind and highlights the complexity
of distinguishing between closely related classes, providing an opportunity to
improve domain adaption methods. The dataset includes 3,264 CAD-rendered
images (32 per part) and 6,146 real images (6 to 137 per part) for UDA and
testing. Rendered images were produced using a Blender-based pipeline with
environment maps, lights, and virtual cameras arranged to ensure varied mesh
orientations. We also use material metadata and apply one of 21 texture materials
to the objects. We render all images at 512x512 pixels. The real photo set consists of
raw images captured under varying conditions using different cameras, including
varied lighting, backgrounds, and environmental factors. ## Download
Download zipped dataset ## Licensing Information
CC BY-NC 4.0 Deed


Please cite our work when using the dataset.
passage: TAGS
#task_categories-image-classification #size_categories-1K<n<10K #license-cc-by-nc-4.0 #arxiv-2310.04757 #region-us 
## Dataset Description
We introduce a challenging dataset for identifying machine parts from real photos,
featuring images of 102 parts from a labeling machine. This dataset was developed
with the complexity of real-world scenarios in mind and highlights the complexity
of distinguishing between closely related classes, providing an opportunity to
improve domain adaption methods. The dataset includes 3,264 CAD-rendered
images (32 per part) and 6,146 real images (6 to 137 per part) for UDA and
testing. Rendered images were produced using a Blender-based pipeline with
environment maps, lights, and virtual cameras arranged to ensure varied mesh
orientations. We also use material metadata and apply one of 21 texture materials
to the objects. We render all images at 512x512 pixels. The real photo set consists of
raw images captured under varying conditions using different cameras, including
varied lighting, backgrounds, and environmental factors.## Download
Download zipped dataset## Licensing Information
CC BY-NC 4.0 Deed


Please cite our work when using the dataset.