This dataset was prepared to aid in the creation of a machine learning algorithm that would classify the white blood cells in thin blood smears of juvenile Visayan warty pigs. The creation of this dataset was deemed imperative because of the limited availability of blood smear images collected from the critically endangered species on the internet. The dataset contains 3,457 images of various types of white blood cells (JPEG) with accompanying cell type labels (XLSX).

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Automated driving in public traffic still faces many technical and legal challenges. However, automating vehicles at low speeds in controlled industrial environments is already achievable today. A reliable obstacle detection is mandatory to prevent accidents. Recent advances in convolutional neural network-based algorithms have made it conceivable to replace distance measuring laser scanners with common monocameras.

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The Active-Passive SimStereo dataset is a simulated dataset created with blender containing high quality both realistic and abstract looking images. Each image pair is rendered in classic RGB domain, as well as Near-Infrared with an active pattern. It is meant to be used as a dataset to study domain transfert between active and passive stereo vision, as well as providing a high quality active stereo dataset, which are far less common than passive stereo datasets.

Instructions: 

Dataset content

The dataset contains 528 image pairs, which are pre-splitted into a test set of 103 image pairs  and a train set of 425 image pairs.

The images have a standard resolution of 640x480 pixels.

The provided ground truth is precise up to floating point precision.

Raw images and ground truth disparities

The raw images in linear color space, as well as the ground truth disparities, are provided as exr images with 32bits floating point precision.

To read the different layers, any exr library should work fine. We are using the python module provided by the exr-tools project by french-paragon:https://github.com/french-paragon/exr-tools

You can also inspect the content of the images with the embeded image viewer in blender or any software capable of reading exr images.

  • The left and right images are contained in the Left, Respectively Right, Layers.
  • The Color, Nir and Disparity Images are contained in the Color, Nir and Disp passes.
  • The Color passe is made up of the standard R, G and B channels
  • The Nir pass is made up of a single A channel
  • The Disp pass is made up of a single D channel

Color managed images

We also exported colormanaged perceptual images (sRGB instead of RGB Linear color space) in standard jpg format if you prefer to use these.

The Images are in the rgbColormanaged and nirColormanaged folder. Each image pair is made up of two images with the same name, but the left or right suffix. You can open them with any software or library able to read .jpg images

 

[EDIT 04-26-2022] Simulation toolkit

We made the simulation toolkit we used to produce the dataset available, so that you can create your own images if you need to. See the simulation.zip file.

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ATTENTION: THIS DATASET DOES NOT HOST ANY SOURCE VIDEOS. WE  PROVIDE ONLY HIDDEN FEATURES GENERATED BY PRE-TRAINED DEEP MODELS AS DATA

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Re-curated Breast Imaging Subset DDSM Dataset (RBIS-DDSM) is a curated version of 849 images from the CBIS-DDSM dataset available online with a permissive copyright license (CC-BY-SA 3.0). The  CBIS-DDSM dataset is an improved version of the DDSM dataset. The authors of the CBIS-DDSM dataset attempted to improve the ground truth by applying simple image processing based methods to enhance the edges without any manual intervention from medical experts in order to segment and annotate masses. However, these annotations (segmentation maps) are inaccurate in most of the images. 

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Object detection via images has advanced quickly over the last few decades, their detection accuracy, categorization, and localization are not consistent. Achieving fast and accurate detection of fashion products in the e-commerce environment is very important for selecting the right category. This is closely related to customer satisfaction and happiness which is a critical aspect. 

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This is the collection of Indian Traffic Sign Detection Dataset. This can be used maily on Traffic Sign detection projects using YOLO. Dataset is in YOLO format. There are 1264 total images in this dataset fully annotated using Labelimg tool. Some augmented datas using techniques like blurring, mosaic etc.. are also present. The dataset has images in 3 different types of traffic signs in India. Dataset is annotated only as one class-Traffic Sign.

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This is the collection of Indian Traffic Sign Detection Dataset. This can be used maily on Traffic Sign detection projects using YOLO. Dataset is in YOLO format. There are 1264 total images in this dataset fully annotated using Labelimg tool. Some augmented datas using techniques like blurring, mosaic etc.. are also present. The dataset has images in 3 different types of traffic signs in India. Dataset is annotated only as one class-Traffic Sign.

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552 Views

Today, the cameras are fixed everywhere, in streets, in vehicles, and in any public area. However, Analysis and extraction of information from images are required. Particularly, in autonomous vehicles and in smart applications that are developed to guide tourists. So, a large dataset of scene text images is an important and difficult factor in the extraction of textual information in natural images. It is the input to any computer vision system.

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This dataset contains a collection of videos consisting of satellite imagery augmented with 3D ship models, accompanied by the ships' corresponding AIS data. The intention of this dataset is for detecting dark ships, which are sea vessels acting maliciously, often while spoofing their AIS data. Multiple datasets exist that consist of satellite imagery of ships, however this dataset has the advantage of including each ships' corresponding AIS data. The simulated ships include both normal and anomalous behavior, whether the anomalous behavior is benign or malicious.

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