adam
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All datasets matching “adam”datacomp200m
Datacomp200m
This is a smaller version of the datacomp_1b dataset.
Filtering was done by taking all rows that had self similarity (inner product) above 0.32. This resulted in 213009083 (213 million) rows.
The results of the datacomp paper suggest that filtering by CLIP score is better than random sampling.
Included in this repo are search indices created using autofaiss, over the text and image embeddings. There are two ways to access metadata, either in .parquet files in the… See the full description on the dataset page: https://huggingface.co/datasets/adams-story/datacomp200m.3D-ADAMRepository for the 3D-ADAM (3D Anomaly Detection in Additive Manufacturing) Dataset. This is the raw data for our complete dataset, separated by part-instance to allow users to utilise the dataset as desired.
We provide a single-camera (using the MechMind-Nano) subset prepared for unsupervised training at anomaly detection, localisation and segementation tasks through the anomalib library in a separate repository: here
Our ArXiv paper can also be found here: 3D-ADAM Dataset
This project has… See the full description on the dataset page: https://huggingface.co/datasets/pmchard/3D-ADAM.imagenet1k-256-wds-latentsThe imagenet1k dataset in the webdataset format
Each image was resized so that the max side resolution is 256, making sure to preserve aspect ratio.
Each image was encoded to latents using the sixteen channel https://huggingface.co/ostris/vae-kl-f8-d16
No cropping was used to encode to latents!
The resulting dataset has images in their original aspect ratio, but much smaller, and encodeded with a vae.
imagenet1k-256-wdsThis is imagenet1k in webdataset format. Images are stored as jpg files. Every image has been resized to a maximum side length of 256. That means that if an image in the original dataset was 1000 by 500, the new size will be 256 by 128. Images with a maximum side length of under 256 were not resized.
The total size of all dataset files is 57.8 GB, there are 1,281,167 rows in the training split and 50,000 rows in the validation split.
stock-market-datasae_bench_results