Context

Richard Zhang's image colourizer model trained on the ImageNet dataset which converts grayscale images to colour images using the L channel of the Lab colour space. The dataset contains necessary files for loading the model and Grayscale images for Image colourization.

Content

Dataset contains necessary files for loading the model and Grayscale images for Image colourization.

Acknowledgements

Richard Zhang who created the model back in 2016 has made this dataset possible.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

🌟 Unlock the potential of advanced computer vision tasks with our comprehensive dataset comprising 15,000 high-quality images. Whether you're delving into segmentation, object detection, or image captioning, our dataset offers a diverse array of visual data to fuel your machine learning models.

🔍 Our dataset is meticulously curated to encompass a wide range of streams, ensuring versatility and applicability across various domains. From natural landscapes to urban environments, from wildlife to everyday objects, our collection captures the richness and diversity of visual content.

📊 Dataset Overview:

🔢 Image Details:

• Format: JPG
• Size Range: Approximately 150 to 300 KB per image

Embark on your computer vision journey and leverage our dataset to develop cutting-edge algorithms, advance research, and push the boundaries of what's possible in visual recognition tasks. Join us in shaping the future of AI-powered image analysis.

Dataset Card for "new-image-dataset"

More Information needed

Open Images is a dataset of ~9M images that have been annotated with image-level labels and object bounding boxes.

The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images, making it the largest existing dataset with object location annotations. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (8.4 per image on average). Moreover, the dataset is annotated with image-level labels spanning thousands of classes.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('open_images_v4', 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/open_images_v4-original-2.0.0.png" alt="Visualization" width="500px">

Labeled Stop sign images suitable for training and evaluating computer vision and deep learning models.

MMID is a large-scale, massively multilingual dataset of images paired with the words they represent collected at the University of Pennsylvania. The dataset is doubly parallel: for each language, words are stored parallel to images that represent the word, and parallel to the word's translation into English (and corresponding images.)

Description

Due to the scarcity of suitable image datasets online related to low-quality images, we created a new dataset specifically for this purpose. The dataset can be used to develop or train models aimed at improving image quality, or serve as a benchmark dataset for evaluating the performance of computer vision on low-quality images. The image image processing code in this dataset is available at https://github.com/pochih-code/Low-quality-image-dataset

Content

Low-quality image dataset is based on the MS COCO 2017 validation images, with images processed into four categories, including lossy compression, image intensity, image noise and image blur. In total, the dataset comprises 100,000 processed images and is modified by humans to ensure that images are valid in the real world.

Format

• coco2017val image blur (25K images)
  • coco_average_4 (5K images)
  • coco_average_6 (5K images)
  • coco_average_8 (5K images)
  • coco_average_10 (5K images)
  • coco_average_12 (5K images)
• coco2017val lossy compression (25K images)
  • coco_compressed_0 (5K images)
  • coco_compressed_20 (5K images)
  • coco_compressed_40 (5K images)
  • coco_compressed_60 (5K images)
  • coco_compressed_80 (5K images)
• coco2017val gamma correction (25K images)
  • coco_gamma_4 (5K images)
  • coco_gamma_8 (5K images)
  • coco_gamma_12 (5K images)
  • coco_gamma_16 (5K images)
  • coco_gamma_20 (5K images)
• coco2017val image noise (25K images)
  • coco_gaussian_10 (5K images)
  • coco_gaussian_20 (5K images)
  • coco_gaussian_30 (5K images)
  • coco_gaussian_40 (5K images)
  • coco_gaussian_50 (5K images) # Acknowledgements "https://cocodataset.org/#home">Microsoft COCO: Common Objects in Context

Dataset Card for "AI-Generated-vs-Real-Images-Datasets"

More Information needed

Labeled Lighter images suitable for AI and computer vision.

Labeled Pencil images suitable for training and evaluating computer vision and deep learning models.

Image Dataset of face images for compuer vision tasks

Dataset comprises 500,600+ images of individuals representing various races, genders, and ages, with each person having a single face image. It is designed for facial recognition and face detection research, supporting the development of advanced recognition systems. By leveraging this dataset, researchers and developers can enhance deep learning models, improve face verification and face identification techniques, and refine… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-recognition-image-dataset.

A diverse image dataset containing clock faces with varying styles, angles, and hand positions, split into training, testing, and validation subsets for accurate time recognition and image classification tasks.

Images of landmarks within the context of their environment

Explore our Ships Image Dataset, featuring 8,506 high-quality images and YOLO v5 annotations. Ideal for AI model training in ship detection and classification.

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

This dataset contains 4,599 high-quality, annotated images of 25 commonly used chemistry lab apparatuses. The images, each containing structures in real-world settings, have been captured from different angles, backgrounds, and distances, while also undergoing variations in lighting to aid in the robustness of object detection models. Every image has been labeled using bounding box annotation in TXT (YOLO) format, alongside the class IDs and normalized bounding box coordinates, making object detection more precise. The annotations and bounding boxes have been built using the Roboflow platform.To achieve a better learning procedure, the dataset has been split into three sub-datasets: training, validation, and testing. The training dataset constitutes 70% of the entire dataset, with validation and testing at 20% and 10% respectively. In addition, all images undergo scaling to a standard of 640x640 pixels while being auto-oriented to rectify rotation discrepancies brought about by the EXIF metadata. The dataset is structured in three main folders - train, valid, and test, and each contains images/ and labels/ subfolders. Every image contains a label file containing class and bounding box data corresponding to each detected object.The whole dataset features 6,960 labeled instances per 25 apparatus categories including beakers, conical flasks, measuring cylinders, test tubes, among others. The dataset can be utilized for the development of automation systems, real-time monitoring and tracking systems, tools for safety monitoring, alongside AI educational tools.

Collection of truck images, from a side point view, used to extract information about truck axles, collected on a highway in the State of São Paulo, Brazil. This is still a work in progress dataset and will be updated regularly, as new images are acquired. More info can be found on: Researchgate Lab Page, OrcID Profiles, or ITS Lab page on Github.

The dataset includes 725 cropped images of trucks, taken with three different cameras, on five different locations.

• 725 images
• Format: JPG
• Resolution: 1920xVarious, 96dpi, 24bits
• Naming pattern:

If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions. We also have a paper that uses this dataset, so if you want to officially cite us in your research, please do so! We appreciate it!

Marcomini, Leandro Arab, and André Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks." arXiv preprint arXiv:2204.01868 (2022).

Compilation of python codes for data preprocessing and VegeNet building, as well as image datasets (zip files).

Image datasets:

1. vege_original : Images of vegetables captured manually in data acquisition stage
2. vege_cropped_renamed : Images in (1) cropped to remove background areas and image labels renamed
3. non-vege images : Images of non-vegetable foods for CNN network to recognize other-than-vegetable foods
4. food_image_dataset : Complete set of vege (2) and non-vege (3) images for architecture building.
5. food_image_dataset_split : Image dataset (4) split into train and test sets
6. process : Images created when cropping (pre-processing step) to create dataset (2).

Curated RGB image dataset for our analysis, splited into training and evalutaion set. Based on ImageNet ILSVRC dataset (Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, 2015).

Dataset Card for Open Images Dataset

This dataset contains images from the Open Images dataset. It includes image URLs, split into training, validation, and test sets.

  Dataset Details





  Dataset Description

Open Images is a dataset of approximately 9 million URLs to images that have been annotated with image-level labels, bounding boxes, object segmentation masks, and visual relationships.

Curated by: Google LLC License: Images: CC BY 2.0 license… See the full description on the dataset page: https://huggingface.co/datasets/bitmind/open-images-v7.