This dataset was originally created by Yilong Zheng. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/wine-label/wine-label-detection.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

Tsinghua Dogs Dataset with ground truth labels for breeds in YOLOv5 format.

Dataset

This dataset was created by Varun Dutt

Contents

Insect benchmark datasets for training, validation and test (train1201.zip, val1201.zip and test1201.zip) with time-lapse images as described in paper:

Bjerge K, Alison J, Dyrmann M, Frigaard C.E., Mann H. M. R., Høye T.T., Accurate detection and identification of insects from camera trap images with deep learning, bioRxiv:10.1101/2022.10.25.513484v1

Labels in YOLO format: ultralytics/yolov5: label format

The annotated training and validation datasets contains insects of nine different species as listed below:

The test dataset contains additional classes of insects.

There are two naming conventions for image (.jpg) and label (.txt) files.

Background images without insects are named:
“X_Seq-YYYYMMDDHHMMSS-snapshot”.
E.g.:
Background image: 12_13-20190704172200-snapshot.jpg
Empty label file: 12_13-20190704172200-snapshot.txt

Images annotated with insects are named:
“SZ_IP-MonthDate_C_Seq-YYYYMMDDHHMMSS”.
E.g.:
Image file: S1_146-Aug23_1_156-20190822133230.jpg
Label file: S1_146-Aug23_1_156-20190822133230.txt

Abbreviations:

YYYYMMDDHHMMSS – Capture timestamp with year, month, date, hour, minutes, and second
Seq – Sequence number created by the motion program to separate images
C – Identification of two cameras with Id=0 or Id=1 in system identified by SZ_IP
MonthDate – Folder name for where the original image were stored in the system
SZ_IP – Identification of five camera systems: S1_123, S2_146, S3_194, S4_199, S5_187 (Two cameras in each system)
X – An index number related to a specific camera and folder ensuring unique file names of background images from different camera systems.

The important information in a filename is system (SZ_IP), camera Id (C) and timestamp (YYYYMMDDHHMMSS).

The three best YOLOv5 models (YOLOv5models.zip) from the paper are available in pytorch format.

All models are tested with YOLOv5 release v7.0 (22-11-2022): ultralytics/yolov5: YOLOv5 in PyTorch

insect1201-bestF1-640v5m.pt: Model no. 6 in Table 2 (F1=0.912)
insect1201-bestF1-1280v5m6.pt: Model no. 8 in Table 2 (F1=0.925)
insect1201-bestF1-1280v5m6.pt: Model no. 10 in Table 2 (F1=0.932)

insects-1201val.yaml: YAML file with label names to train YOLOv5

trainInsects-1201m.sh: Linux bash shell script with parameters to train YOLOv5m6
valInsectsF1-1201.sh: Linux bash shell script with parameters to validated models

Dataset

This dataset was created by Mark Antonyo

Contents

WeedCrop Image Dataset Data Description It includes 2822 images. Images are annotated in YOLO v5 PyTorch format. -Train directory contains 2469 images and respective labels in yolov5 Pytorch format. -Validation directory contains 235 images and respective labels in yolov5 Pytorch format. -Test directory contains 118 images and respective labels in yolov5 Pytorch format. Reference- https://www.kaggle.com/datasets/vinayakshanawad/weedcrop-image-dataset

Dataset

This dataset was created by Mark Antonyo

Contents

Description:

👉 Download the dataset here

This dataset consists of meticulously annotated images of tire side profiles, specifically designed for image segmentation tasks. Each tire has been manually labeled to ensure high accuracy, making this dataset ideal for training machine learning models focused on tire detection, classification, or related automotive applications.

The annotations are provided in the YOLO v5 format, leveraging the PyTorch framework for deep learning applications. The dataset offers a robust foundation for researchers and developers working on object detection, autonomous vehicles, quality control, or any project requiring precise tire identification from images.

Download Dataset

Data Collection and Labeling Process:

Manual Labeling: Every tire in the dataset has been individually labeled to guarantee that the annotations are highly precise, significantly reducing the margin of error in model training.

Annotation Format: YOLO v5 PyTorch format, a highly efficient and widely used format for real-time object detection systems.

Pre-processing Applied:

Auto-orientation: Pixel data has been automatically oriented, and EXIF orientation metadata has been stripped to ensure uniformity across all images, eliminating issues related to

image orientation during processing.

Resizing: All images have been resized to 416×416 pixels using stretching to maintain compatibility with common object detection frameworks like YOLO. This resizing standardizes the image input size while preserving visual integrity.

Applications:

Automotive Industry: This dataset is suitable for automotive-focused AI models, including tire quality assessment, tread pattern recognition, and autonomous vehicle systems.

Surveillance and Security: Use cases in monitoring systems where identifying tires is crucial for vehicle recognition in parking lots or traffic management systems.

Manufacturing and Quality Control: Can be used in tire manufacturing processes to automate defect detection and classification.

Dataset Composition:

Number of Images: [Add specific number]

File Format: JPEG/PNG

Annotation Format: YOLO v5 PyTorch

Image Size: 416×416 (standardized across all images)

This dataset is sourced from Kaggle.

Dataset

This dataset was created by Mark Antonyo

Contents

Mechanical Parts Dataset

The dataset consists of a total of 2250 images obtained by downloading from various internet platforms. Among the images in the dataset, there are 714 images with bearings, 632 images with bolts, 616 images with gears and 586 images with nuts. A total of 10597 manual labeling processes were carried out in the dataset, including 2099 labels belonging to the bearing class, 2734 labels belonging to the bolt class, 2662 labels belonging to the gear class and 3102 labels belonging to the nut class.

Folder Content

The created dataset is divided into 3 as 80% train, 10% validation and 10% test. In the "Mechanical Parts Dataset" folder, there are three separate folders as "train", "test" and "val". In each of these three folders there are folders named "images" and "labels". Images are kept in the "images" folder and tag information is kept in the "labels" folder.

Finally, inside the folder there is a yaml file named "mech_parts_data" for the Yolo algorithm. This file contains the number of classes and class names.

Images and Labels

The dataset was prepared in accordance with the Yolov5 algorithm.
For example, the tag information of the image named "2a0xhkr_jpg.rf.45a11bf63c40ad6e47da384fdf6bb7a1.jpg" is stored in the txt file with the same name. The tag information (coordinates) in the txt file are as follows: "class x_center y_center width height".

Related paper: doi.org/10.5281/zenodo.7496767

Mechanical Parts Dataset

The dataset consists of a total of 2250 images obtained by downloading from various internet platforms. Among the images in the dataset, there are 714 images with bearings, 632 images with bolts, 616 images with gears and 586 images with nuts. A total of 10597 manual labeling processes were carried out in the dataset, including 2099 labels belonging to the bearing class, 2734 labels belonging to the bolt class, 2662 labels belonging to the gear class and 3102 labels belonging to the nut class.

Folder Content

The created dataset is divided into 3 as 80% train, 10% validation and 10% test. In the "Mechanical Parts Dataset" folder, there are three separate folders as "train", "test" and "val". In each of these three folders there are folders named "images" and "labels". Images are kept in the "images" folder and tag information is kept in the "labels" folder.

Finally, inside the folder there is a yaml file named "mech_parts_data" for the Yolo algorithm. This file contains the number of classes and class names.

Images and Labels

The dataset was prepared in accordance with the Yolov5 algorithm.
For example, the tag information of the image named "2a0xhkr_jpg.rf.45a11bf63c40ad6e47da384fdf6bb7a1.jpg" is stored in the txt file with the same name. The tag information (coordinates) in the txt file are as follows: "class x_center y_center width height".

Update 05.01.2023

***Pascal voc and coco json formats have been added.***

Related paper: doi.org/10.5281/zenodo.7496767

Context

One of my passions is playing board games with my friends. However one of them lives abroad and so we like to stream the game when playing with him. However instead of just having a normal stream, I wanted to show some additional information about the monsters that are on the game board. This originated in a fun project to train CNNs in order to detect these monsters.

Content

To have enough training data, I made a little project in UE4 to generate these training images. For each image there is a mask for every monster that appears in it. The dataset also includes annotations for the train images in the COCO format (annotations.json) and labes for the bounding box in Darknet format in the folder labels.

There is a training and validation subset for the images, labels and masks folders. The structure is as follows: for the first training image containing an earth_demon and harrower_infester:

• The image is stored at images/train/image_1.png
• The label-file is stored at labels/train/label_1.png. This file contains two lines. One line for each monster. A line is constructed as follows: class_id center_x center_y width height. Note that the position and dimensions are relative to the image width and height.
• There are two mask images located at masks/train. One is named image_1_mask_0_harrower_infester.png and the other image_1_mask_1_earth_demon.png.

The code for generating this dataset and training a MaskRCNN and YoloV5 model can be found at https://github.com/ericdepotter/Gloomhaven-Monster-Recognizer.

Acknowledgements

I took pictures for the images of the monsters myself. The images of the game tiles I obtained from this collection of Gloomhaven assets.

Inspiration

This is a classic object detection or object segmentation problem.

BackgroundUrogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African continent. The gold-standard diagnosis is still direct observation of Schistosoma haematobium eggs in urine samples by optical microscopy. Novel diagnostic techniques based on digital image analysis by Artificial Intelligence (AI) tools are a suitable alternative for schistosomiasis diagnosis.MethodologyDigital images of 24 urine sediment samples were acquired in non-endemic settings. S. haematobium eggs were manually labeled in digital images by laboratory professionals and used for training YOLOv5 and YOLOv8 models, which would achieve automatic detection and localization of the eggs. Urine sediment images were also employed to perform binary classification of images to detect erythrocytes/leukocytes with the MobileNetv3Large, EfficientNetv2, and NasNetLarge models. A robotized microscope system was employed to automatically move the slide through the X-Y axis and to auto-focus the sample.ResultsA total number of 1189 labels were annotated in 1017 digital images from urine sediment samples. YOLOv5x training demonstrated a 99.3% precision, 99.4% recall, 99.3% F-score, and 99.4% mAP0.5 for S. haematobium detection. NasNetLarge has an 85.6% accuracy for erythrocyte/leukocyte detection with the test dataset. Convolutional neural network training and comparison demonstrated that YOLOv5x for the detection of eggs and NasNetLarge for the binary image classification to detect erythrocytes/leukocytes were the best options for our digital image database.ConclusionsThe development of low-cost novel diagnostic techniques based on the detection and identification of S. haematobium eggs in urine by AI tools would be a suitable alternative to conventional microscopy in non-endemic settings. This technical proof-of-principle study allows laying the basis for improving the system, and optimizing its implementation in the laboratories.

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.