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--- |
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license: cc-by-nc-4.0 |
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pretty_name: imagenet3d |
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--- |
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## ImageNet3D |
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We present **ImageNet3D**, a large dataset for general-purpose object-level 3D understanding. ImageNet3D augments 200 categories from the ImageNet dataset with 2D bounding box, 3D pose, 3D location annotations, and image captions interleaved with 3D information. |
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Refer to [github.com/wufeim/imagenet3d](https://github.com/wufeim/imagenet3d) for the full documentation and sample preprocessing code for ImageNet3D. |
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### Download Data |
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**ImageNet3D-v1.0:** Directly download from the HuggingFace WebUI, or on a server, run |
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```sh |
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wget https://huggingface.co/datasets/ccvl/ImageNet3D/resolve/main/imagenet3d_v1.zip |
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``` |
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**Future updates:** We are working on annotating more object categories and improving the quality of current annotations. |
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The next update is planned to be released by the end of Jan 2025. Please let us know if you have any suggestions for future updates. |
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### Example Usage |
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```py |
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from PIL import Image |
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import numpy as np |
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img_path = 'imagenet3d/bed/n02818832_13.JPEG' |
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annot_path = 'imagenet3d/bed/n02818832_13.npz' |
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img = np.array(Image.open(img_path).convert('RGB')) |
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annot = dict(np.load(annot_path, allow_pickle=True))['annotations'] |
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# Number of objects |
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num_objects = len(annot) |
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# Annotation of the first object |
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azimuth = annot[0]['azimuth'] # float, [0, 2*pi] |
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elevation = annot[0]['elevation'] # float, [0, 2*pi] |
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theta = annot[0]['theta'] # float, [0, 2*pi] |
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cad_index = annot[0]['cad_index'] # int |
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distance = annot[0]['distance'] # float |
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viewport = annot[0]['viewport'] # int |
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img_height = annot[0]['height'] # numpy.uint16 |
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img_width = annot[0]['width'] # numpy.uint16 |
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bbox = annot[0]['bbox'] # numpy.ndarray, (x1, y1, x2, y2) |
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category = annot[0]['class'] # str |
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principal_x = annot[0]['px'] # float |
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principal_y = annot[0]['py'] # float |
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# label indicating the quality of the object, occluded or low quality |
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object_status = annot[0]['object_status'] # str, one of ('status_good', 'status_partially', 'status_barely', 'status_bad') |
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# label indicating if multiple objects from same category very close to each other |
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dense = annot[0]['dense'] # str, one of ('dense_yes', 'dense_no') |
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``` |
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