Datasets:
Dataset Viewer (First 5GB)
depth.npy
sequencelengths 448
448
| jpg
imagewidth (px) 608
608
| __key__
stringlengths 14
14
| __url__
stringclasses 7
values |
|---|---|---|---|
[["2.104","2.104","2.104","2.104","2.104","2.104","2.104","2.104","2.133","2.133","2.133","2.133","2(...TRUNCATED) | train_00000000 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
|
[["2.162","2.174","2.174","2.174","2.174","2.174","2.174","2.174","2.174","2.174","2.174","2.174","2(...TRUNCATED) | train_00000001 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
|
[["0.0","0.0","0.0","0.0","0.0","0.0","0.0","0.0","0.0","0.0","0.0","0.0","0.0","0.0","0.0","0.0","0(...TRUNCATED) | train_00000002 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
|
[["3.89","3.89","3.89","3.89","3.89","3.89","3.89","3.89","3.89","3.89","3.89","3.89","3.89","3.89",(...TRUNCATED) | train_00000003 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
|
[["1.425","1.425","1.425","1.425","1.425","1.425","1.425","1.425","1.425","1.438","1.438","1.438","1(...TRUNCATED) | train_00000004 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
|
[["2.865","2.865","2.865","2.865","2.865","2.865","2.865","2.865","2.865","2.865","2.865","2.865","2(...TRUNCATED) | train_00000005 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
|
[["2.611","2.611","2.611","2.611","2.611","2.611","2.611","2.623","2.611","2.611","2.611","2.623","2(...TRUNCATED) | train_00000006 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
|
[["2.975","2.975","2.975","2.975","2.975","2.975","2.975","2.988","2.988","2.988","2.988","2.988","2(...TRUNCATED) | train_00000007 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
|
[["2.945","2.945","2.945","2.945","2.945","2.945","2.945","2.945","2.945","2.945","2.945","2.945","2(...TRUNCATED) | train_00000008 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
|
[["0.786","0.786","0.786","0.786","0.786","0.786","0.786","0.786","0.786","0.786","0.786","0.786","0(...TRUNCATED) | train_00000009 | "hf://datasets/adams-story/nyu-depthv2-wds@507fbf1c8623803bdac4466913495bfb8950d619/nyu-depth-train-(...TRUNCATED) |
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Dataset Card for nyu-depthv2-wds
This is the NYU DepthV2 dataset, converted into the webdataset format. https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/
There are 47584 samples in the training split, and 654 samples in the validation split.
I shuffled both the training samples, and the validation samples.
I also cropped 16 pixels from all sides of the image, and depth image. I did this because there is a white border around all images.
This is an example of the border artifacts in the images:
Dataset Details
Dataset Description
Each sample contains a jpg image, and a numpy array of depth data. Depth maps are stored in float16.
Dataset Sources [optional]
See https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
Uses
Train a depth prediction model on the train split, and test on the val split!
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