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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
17,713 datasets
A collection of images of Magic: The Gathering cards for four creature types: elf, goblin, knight, and zombie. The images were sourced from the official Gatherer database. The specific number of rows, columns, and total size is not provided.
Comprising model predictions for a summarization task on the CNN/DailyMail dataset, generated by the facebook/bart-large-cnn model via the AutoTrain platform. The specific split used is the 'train' split of the dataset. The number of rows, columns, and file size are unknown.
Aggregating model predictions for a summarization task on the CNN DailyMail dataset's test split. The predictions were generated by the model 'pszemraj/long-t5-tglobal-base-16384-booksum-V11' using the AutoTrain platform. The specific row count, column structure, and data size are not provided in the input.
This collaborative repository indexes RGB-D and LiDAR data sources to support the paper 'A Survey on RGB-D Datasets.' Maintained by alelopes and last updated in July 2022, it serves as a directory for researchers seeking depth-estimation benchmarks.
A collection of images of rice grains for classification tasks. It is associated with published research on classifying rice varieties using deep learning methods. The specific number of images and features is not provided in the input.
Comprising model predictions for a summarization task on the CNN/DailyMail dataset's test split. The predictions were generated by the pszemraj/long-t5-tglobal-base-16384-book-summary model using the AutoTrain evaluator in July 2022.
Featuring model predictions for a summarization task on the CNN/DailyMail dataset's test split. The predictions were generated by the pszemraj/long-t5-tglobal-base-16384-book-summary model via the AutoTrain platform. The row count, column count, and specific data fields are unknown.
A collection of model predictions for a summarization task on the CNN DailyMail dataset's test split. The predictions were generated by the pszemraj/led-base-book-summary model via the AutoTrain platform. The specific row count, column count, and data size are unknown.
Multiple datasets categorized for Optical Character Recognition (OCR) tasks. Resources within the repository support text recognition and document analysis.
Model predictions for a summarization task, generated by the patrickvonplaten/bert2bert_cnn_daily_mail model on the CNN/DailyMail dataset's training split. The data was created by the AutoTrain Evaluator platform and last updated on July 12, 2022.
300 high-resolution RGB images containing 4,432 manually annotated grape clusters across 5 distinct wine grape varieties. The dataset provides instance-level annotations including bounding boxes and pixel-wise binary masks for Cabernet Franc, Cabernet Sauvignon, Chardonnay, Merlot, and Syrah cultivars in field conditions.
A collection of model predictions from a T5-large model evaluated on the CNN/DailyMail summarization task. The predictions were generated by the AutoTrain platform on the 'train' split of the dataset. The specific number of rows, columns, and data size are unknown.
Comprising model predictions generated by AutoTrain for the summarization task on the CNN DailyMail dataset. The predictions were produced by the tuner007/pegasus_summarizer model on the train split of the dataset version 3.0.0. The number of rows, columns, and specific data fields are not provided in the input.
The COCO dataset is a large-scale collection for object detection, segmentation, and captioning tasks. It was authored by ydshieh and last updated on February 14, 2022.
The Scene UNderstanding (SUN) database contains 108,754 images across 899 scene categories, with at least 100 images per category. The images are provided for research purposes in formats including JPG, PNG, or GIF.
Car part images categorized for semantic segmentation and object detection tasks provided by the DSMLR lab at IT-KMITL. This collection enables the training of computer vision models to identify and delineate specific automotive components within visual data.
The FUNSD dataset is a collection of noisy scanned documents for form understanding tasks. It was created by JetsonEarth and last updated in July 2022.
Encompassing model predictions generated by AutoTrain for a multi-class image classification task on the CIFAR-10 dataset. The predictions were produced by the model 'karthiksv/vit-base-patch16-224-cifar10'. The specific number of rows, columns, and data size are unknown.
Featuring model predictions generated by the AutoTrain platform for a multi-class image classification task on the CIFAR-10 dataset. The predictions were produced by the 'abhishek/autotrain_cifar10_vit_base' model. The specific number of rows, columns, and file formats are unknown.
Featuring model predictions generated by AutoTrain for a multi-class image classification task. The evaluated model is abhishek/convnext-tiny-finetuned-dogfood, and the source dataset is lewtun/dog_food. The row count, column count, and specific data fields are unknown.