K=3,train And Val Split

## Overview

K=3,train And Val Split is a dataset for object detection tasks - it contains Saba Shekari annotations for 3,206 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The KITTI val split is a subset of the KITTI dataset, used for validation and testing.

This is a cleaned version of the Quora dataset that's been configured with a train-test-val split.

Train : For training model Test : For running experiments and comparing different OSS models and closed sourced models Val : Only to be used at the end!

Colab Notebook to reproduce : https://colab.research.google.com/drive/1dGjGiqwPV1M7JOLfcPEsSh3SC37urItS?usp=sharing

Dataset

This dataset was created by Dewizzz

Contents

Dataset

This dataset was created by Büşra Ertekin

Contents

Bradley/fineweb-sample-100BT_over-2048-tokens-subset-split-processed-val dataset hosted on Hugging Face and contributed by the HF Datasets community

dataset_info: features: -name: image dtype: image -name: question dtype: string -name: caption dtype: string

splits: -name: train num_bytes: 1,572,864 num_examples: 40

-name: test num_bytes: 764,825.6 num_examples: 20

-name: val num_bytes: 961,740.8 num_examples: 20

configs: data_files: -split: train path: data/train -split: test path: data/test -split: val path: data/val

Note

This dataset doesn't contain source data, only train-val description.

Caltech 256 original description

We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category. Caltech-256 is collected in a similar manner with several improvements: a) the number of categories is more than doubled, b) the minimum number of images in any category is increased from 31 to 80, c) artifacts due to image rotation are avoided and d) a new and larger clutter category is introduced for testing background rejection. We suggest several testing paradigms to measure classification performance, then benchmark the dataset using two simple metrics as well as a state-of-the-art spatial pyramid matching algorithm. Finally we use the clutter category to train an interest detector which rejects uninformative background regions.

Citation

Griffin, G., Holub, A., & Perona, P. (2022). Caltech 256 (1.0) [Data set]. CaltechDATA.

https://doi.org/10.22002/D1.20087

Dataset Division

Unique_query : contains unique queries that are not present in train, val, test split. Used for testing unseen understandability of models. Train_all : contains the unsplit train, test, val datapoints. train : train split val : val split test : test split

Original Paper : LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments Bibtext: @inproceedings{upadhya-t-y-s-s-2025-lexclipr, title = "{L}ex{CL}i{PR}: Cross-Lingual Paragraph Retrieval from Legal… See the full description on the dataset page: https://huggingface.co/datasets/rohit-upadhya/lexclipr.

Split 6 Train Track KFCV

## Overview

Split 6 Train Track KFCV is a dataset for object detection tasks - it contains Train Track Damage annotations for 1,296 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for Dataset Name

Generation I pokemon images dataset (35 pokemon).

  Dataset Details

This dataset was curated using pokemon images found on bulbapedia. It contains 35 randomly selected Generation I pokemon, with 7 images per pokemon.

  Dataset Description

The dataset has train/test/val splits. The train split contains 4 images per pokemon, the test & val splits contain either 2 or 1 image per pokemon. The following images were selected from… See the full description on the dataset page: https://huggingface.co/datasets/VictoriaDerks/pokemon-classification.

Val_split

## Overview

Val_split is a dataset for object detection tasks - it contains Good Broke Lose Uncovered Circle 7GO9 annotations for 1,489 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Split 10 Train Track KFCV

## Overview

Split 10 Train Track KFCV is a dataset for object detection tasks - it contains Train Track Damage annotations for 1,296 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

SA-Co/VEval – YT-Temporal-1B: Val

SA-Co/VEval is an evaluation dataset for promptable concept segmentation (PCS) in images developed by Meta for the Segment Anything 3 model (SAM 3). The dataset contains videos paired with text labels (also referred as Noun Phrases aka NPs), each annotated exhaustively with masks on all object instances that match the label.

• SA-Co/VEval - SA-V: videos are from the SA-V dataset
• SA-Co/VEval - YT-Temporal-1B: videos are from the YT-Temporal-1B
• SA-Co/VEval - SmartGlasses: egocentric videos from Smart Glasses

This Project allows you to explore YT-Temporal-1B: Val, which is the val split from the YT-Temporal-1B subset. You can see the test split at YT-Temporal-1B: Test.

Download Instructions

The full SA-Co/VEval data is available in its canonical, eval-ready form below.

Download SA-V video frames: https://sa-co.roboflow.com/veval/saco_sav.zip

Download YT-1B video frames: https://sa-co.roboflow.com/veval/saco_yt1b.zip

Download SmartGlasses video frames: https://sa-co.roboflow.com/veval/saco_sg.zip

Download ground truth annotations: https://sa-co.roboflow.com/veval/gt-annotations.zip

Download the full bundle: https://sa-co.roboflow.com/veval/all.zip

Explore all SA-Co/VEval datasets

The Sa-Co/VEval dataset covers 3 image sources. The image sources are: SA-V, YT-Temporal-1B, SmartGlasses.

Explore all: SA-Co/VEval on Roboflow Universe

Read Meta's data license for SA-Co/VEval: License

SA-Co/VEval – SA-V: Val

SA-Co/VEval is an evaluation dataset for promptable concept segmentation (PCS) in images developed by Meta for the Segment Anything 3 model (SAM 3). The dataset contains videos paired with text labels (also referred as Noun Phrases aka NPs), each annotated exhaustively with masks on all object instances that match the label.

• SA-Co/VEval - SA-V: videos are from the SA-V dataset
• SA-Co/VEval - YT-Temporal-1B: videos are from the YT-Temporal-1B
• SA-Co/VEval - SmartGlasses: egocentric videos from Smart Glasses

This Project allows you to explore SA-V: Val, which is the val split from the SA-V subset. You can see the test split at SA-V: Test.

Download Instructions

The full SA-Co/VEval data is available in its canonical, eval-ready form below.

Download SA-V video frames: https://sa-co.roboflow.com/veval/saco_sav.zip

Download YT-1B video frames: https://sa-co.roboflow.com/veval/saco_yt1b.zip

Download SmartGlasses video frames: https://sa-co.roboflow.com/veval/saco_sg.zip

Download ground truth annotations: https://sa-co.roboflow.com/veval/gt-annotations.zip

Download the full bundle: https://sa-co.roboflow.com/veval/all.zip

Explore all SA-Co/VEval datasets

The Sa-Co/VEval dataset covers 3 image sources. The image sources are: SA-V, YT-Temporal-1B, SmartGlasses.

Explore all: SA-Co/VEval on Roboflow Universe

Read Meta's data license for SA-Co/VEval: License

Dataset

This dataset was created by Gabriel Baiduc

Released under Apache 2.0

Contents

Nemotron Post-Training Samples with Train/Val/Test Splits

This dataset contains structured train/validation/test splits from the nvidia/Llama-Nemotron-Post-Training-Dataset, with both tagged and untagged versions for different training scenarios.

  Attribution

This work is derived from the Llama-Nemotron-Post-Training-Dataset-v1.1 by NVIDIA Corporation, licensed under CC BY 4.0. Original Dataset: nvidia/Llama-Nemotron-Post-Training-Dataset Original Authors: NVIDIA… See the full description on the dataset page: https://huggingface.co/datasets/brandolorian/nemotron-post-training-samples-splits.

A collection of 3 referring expression datasets based off images in the COCO dataset. A referring expression is a piece of text that describes a unique object in an image. These datasets are collected by asking human raters to disambiguate objects delineated by bounding boxes in the COCO dataset.

RefCoco and RefCoco+ are from Kazemzadeh et al. 2014. RefCoco+ expressions are strictly appearance based descriptions, which they enforced by preventing raters from using location based descriptions (e.g., "person to the right" is not a valid description for RefCoco+). RefCocoG is from Mao et al. 2016, and has more rich description of objects compared to RefCoco due to differences in the annotation process. In particular, RefCoco was collected in an interactive game-based setting, while RefCocoG was collected in a non-interactive setting. On average, RefCocoG has 8.4 words per expression while RefCoco has 3.5 words.

Each dataset has different split allocations that are typically all reported in papers. The "testA" and "testB" sets in RefCoco and RefCoco+ contain only people and only non-people respectively. Images are partitioned into the various splits. In the "google" split, objects, not images, are partitioned between the train and non-train splits. This means that the same image can appear in both the train and validation split, but the objects being referred to in the image will be different between the two sets. In contrast, the "unc" and "umd" splits partition images between the train, validation, and test split. In RefCocoG, the "google" split does not have a canonical test set, and the validation set is typically reported in papers as "val*".

Stats for each dataset and split ("refs" is the number of referring expressions, and "images" is the number of images):

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('ref_coco', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/ref_coco-refcoco_unc-1.1.0.png" alt="Visualization" width="500px">

This dataset consists of synthetically spoken captions for the STAIR dataset. Following the same methodology as Chrupała et al. (see article | dataset | code) we generated speech for each caption of the STAIR dataset using Google's Text-to-Speech API.

This dataset was used for visually grounded speech experiments (see article accepted at ICASSP2019).

@INPROCEEDINGS{8683069, author={W. N. {Havard} and J. {Chevrot} and L. {Besacier}}, booktitle={ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Models of Visually Grounded Speech Signal Pay Attention to Nouns: A Bilingual Experiment on English and Japanese}, year={2019}, volume={}, number={}, pages={8618-8622}, keywords={information retrieval;natural language processing;neural nets;speech processing;word processing;artificial neural attention;human attention;monolingual models;part-of-speech tags;nouns;neural models;visually grounded speech signal;English language;Japanese language;word endings;cross-lingual speech-to-speech retrieval;grounded language learning;attention mechanism;cross-lingual speech retrieval;recurrent neural networks.}, doi={10.1109/ICASSP.2019.8683069}, ISSN={2379-190X}, month={May},}

The dataset comprises the following files :

mp3-stair.tar.gz : MP3 files of each caption in the STAIR dataset. Filenames have the following pattern imageID_captionID, where both imageID and captionID correspond to those provided in the original dataset (see annotation format here)

dataset.mfcc.npy : Numpy array with MFCC vectors for each caption. MFCC were extracted using python_speech_features with default configuration. To know to which caption the MFCC vectors belong to, you can use the files dataset.words.txt and dataset.ids.txt.

dataset.words.txt : Captions corresponding to each MFCC vector (line number = position in Numpy array, starting from 0)

dataset.ids.txt : IDs of the captions (imageID_captionID) corresponding to each MFCC vector (line number = position in Numpy array, starting from 0)

Splits

test

test.txt : captions comprising the test split

test_ids.txt: IDs of the captions in the test split

test_tagged.txt : tagged version of the test split

test-alignments.json.zip : Forced alignments of all the captions in the test split. (dictionary where the key corresponds to the caption ID in the STAIR dataset). Due to an unknown error during upload, the JSON file had to be zipped...

train

train.txt : captions comprising the train split

train_ids.txt : IDs of the captions in the train split

train_tagged.txt : tagged version of the train split

val

val.txt : captions comprising the val split

val_ids.txt : IDs of the captions in the val split

val_tagged.txt : tagged version of the val split

.db versions of the train/test/val splits as described by https://gitlab.com/matschreiner/Transition1x.For Fall 2023 CS224W Final Project