Here are a few use cases for this project:

1. Digital Inventory Management: The Segmentation computer vision model could be used to identify the specific labels (QR Codes, Barcodes, or digits) on boxes within a warehouse. When boxes are stacked in different orientations, this identification can ensure optimal tracking and warehouse management.

2. Parcel Sorting in Logistic Companies: The model could be employed in automatic parcel sorting systems. It can recognize different types of coded information, aiding in the process of classifying and sorting packages more efficiently, improving overall logistics operations.

3. Retail Checkout Systems: A use case can be found in retail checkout systems where the model can help in real-time identification and processing of product codes, be it barcodes or QR codes, making the checkout process quicker and more efficient.

4. Automated Library Systems: The model can be used to identify the labels on books in various orientations to update the library's database and streamline book tracking, lending, and inventory operations.

5. Mass Transport Systems: In transport hubs or stations (airports, train stations), this kind of model can be used to identify and sort luggage or cargo based on tags, speeding up the loading/unloading process and reducing errors.

Here are a few use cases for this project:

1. Urban Planning and Development: Utilizing the "Buildings Instance Segmentation" model to analyze aerial images of a city, urban planners can identify different types of buildings and their distribution to make informed decisions about zoning, infrastructure, and future developments.

2. Damage Assessment and Emergency Response: In the aftermath of natural disasters, the model can be used to analyze aerial images to quickly identify damaged or destroyed buildings, helping emergency responders prioritize rescue efforts and allocate resources more efficiently.

3. Real Estate Market Analysis: Real estate professionals can use the model to analyze aerial views of neighborhoods, identifying different types of buildings and their locations to offer better insights into neighborhood characteristics and trends for potential property buyers.

4. Energy Efficiency and Environmental Impact Analysis: By identifying different building classes and their distribution, researchers can evaluate energy consumption patterns and develop strategies for improving energy efficiency and reducing the environmental impact in urban areas.

5. Historical Preservation and Cultural Heritage: The model can be employed to identify and track the presence of culturally significant or historically important buildings for preservation efforts, ensuring their protection and integration into urban development plans.

Here are a few use cases for this project:

1. Infrastructure Maintenance: The segmentation model can be used to identify and classify different types of cracks in public infrastructure like buildings, bridges, roads, and railways. This could assist in early detection and maintenance, preventing costly repairs or accidents.

2. Geology and Environmental Studies: The model could be used to analyze cracks in the ice in the polar regions, assisting in climate research by monitoring the melting rate of glaciers and its impact on sea levels.

3. Automotive Industry: The model can be used in the analysis of wear and tear in vehicles, particularly in tires where cracks could lead to potentially dangerous blowouts.

4. Aerospace Industry: The model can be used to detect cracks in aircraft fuselage or wings during routine maintenance checks. Early detection will reduce the risk of catastrophic failure during flights.

5. Energy Industry: In wind turbine maintenance, the model could be used to detect cracks in turbine blades. Early detection can prevent failures, prolonging the lifespan of the turbines and increasing efficiency.

Recent advances in language-image pre-training has witnessed the emerging field of building transferable systems that can effortlessly adapt to a wide range of computer vision & multimodal tasks in the wild. This also poses a challenge to evaluate the transferability of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. "Segmentation in the Wild (SegInW)" Challenge is a part of X-Decoder, that proposed a new benchmark to evaluate the transfer ability of pre-trained vision models. This benchmark presents a diverse set of downstream segmentation datasets, measuring the ability of pre-training models on both the segmentation accuracy and their transfer efficiency in a new task, in terms of training examples and trainable parameters. This SegInW Challenge consists of 25 free public Segmentation datasets, crowd-sourced on roboflow.com. For more details about the challenge submission format, please refer to X-Decoder for SGinW.

The material segmentation dataset comprises 3817 images gathered from the Virginia Department of Transportation (VDOT) Bridge Inspection Reports. There were four classes of material in the dataset: [concrete, steel, metal decking, and background]. The data was randomly sorted into training and testing using a custom script. 10% percent were reserved as the test set, and 90% were used as the training set. Therefore, there were 381 images in the test set and 3436 images in the training set. The original and the rescaled images used for training have been included. The images were resized to 512x512 for training and testing the DeeplabV3+ model. After training with the DeeplabV3+ model (DOI: 10.7294/16628620), we were able to achieve an F1-score of 94.2%. Details of the dataset, training process, and code can be referenced by reading the associated journal article. The GitHub repository information may be found in the journal article.If you are using the dataset in your work, please include both the journal article and the dataset citation.

The dataset includes 458 hi-res images together with their alpha maps (BW) indicating the crack presence. The ground truth for semantic segmentation has two classes to conduct binary pixelwise classification. The photos are captured in various buildings located in Middle East Technical University.

You can access a larger dataset containing images with 227x227 px dimensions for classification which are produced from this dataset from http://dx.doi.org/10.17632/5y9wdsg2zt.1 .

Unsupervised Learning Online Retail Customer Segmentation

Video object segmentation dataset than contains 150 video sequences. Each sequence contains multiple objects and their high quality segmentation mask in each of the frames.

The data was collected from the Virginia Department of Transportation (VDOT) Bridge Inspection Reports. The data was semantically annotated following the corrosion condition state guidelines stated in the American Association of State Highway and Transportation Officials (AASHTO) and Bridge Inspector's Reference Manual (BIRM). There were four corrosion class categories: [good, fair, poor, severe]. The dataset consisted of 440 finely annotated images and was randomly split into 396 training images and 44 testing images. The images were resized to 512x512 for training and testing the DeeplabV3+ model. The original and resized images are included. After training with the DeeplabV3+ model (DOI: 10.7294/16628668), we were able to receive an F1 score of 86.67. More details of the training, the results, the dataset, and the code may be referenced in the journal article. The GitHub repository information may be found in the journal article.If you are using the dataset in your work, please include both the journal article and the dataset citation.

Collection of rust segmentation images. Sources include:

https://universe.roboflow.com/faisal-hazry-orm7q/yolov8corrosion

Person Segmentation is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the surveillance industry. The dataset consists of 21011 images with 21011 labeled objects belonging to 1 single class (person)

Road Segmentation Dataset

This dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on TrainingData to buy the dataset

The dataset can be utilized for enhancing computer vision algorithms involved in road surveillance, navigation, and intelligent transportation systemsand and in autonomous driving systems.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fb0789a0ec8075d9c7abdb0aa9faced59%2FFrame%2012.png?generation=1694606364403023&alt=media" alt="">

DATASETS WITH VEHICLES :

• License Plates - 1 209 438 OCR Plates
• Car Object Detection & Tracking
• Vehicle Images & Segmentation
• Parking Space Detection & Classification Dataset
• Videos around cars

Dataset structure

• images - contains of original images of roads
• masks - includes segmentation masks created for the original images
• annotations.xml - contains coordinates of the polygons, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.

Сlasses:

• road_surface: surface of the road,
• marking: white and yellow marking on the road,
• road_sign: road signs,
• car: cars on the road,
• background: side of the road and surronding objects

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fa74a4214f4dd89a35527ef008abfc151%2Fcarbon.png?generation=1694608637609153&alt=media" alt="">

Roads Segmentation might be made in accordance with your requirements.

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/data-market to discuss your requirements, learn about the price and buy the dataset

TrainingData provides high-quality data annotation tailored to your needs

keywords: road surface, road scene, off-road, vehicle segmentation dataset, semantic segmentation for self driving cars, self driving cars dataset, semantic segmentation for autonomous driving, car segmentation dataset, car dataset, car images, car parts segmentation, self-driving cars deep learning, cctv, image dataset, image classification, semantic segmentation

Car Segmentation dataset encompasses 211 car side view images, meticulously annotated into four distinct classes: car, wheel, lights, and window. The dataset creation process involved a comprehensive approach, including image gathering through various methods and detailed mask annotation using VoTT, ultimately culminating in a valuable resource for training and developing object recognition models. For those interested in utilizing the dataset without manual annotation, a pre-annotated dataset has been thoughtfully provided by the creator for use in training models.

Here are a few use cases for this project:

1. Agricultural Monitoring: The chili segmentation model can be used in agriculture for monitoring and analyzing chili plant health, enabling farmers to track the growth of leaves and fruit-chilies, and make data-driven decisions regarding crop management, fertilization, and pesticide application.

2. Precision Harvesting: The model can be incorporated into automated harvesting systems, helping to differentiate between mature fruit-chilies and leaves or identify ripe fruit only. This ensures a more efficient and selective harvest, reducing labor costs and contributing to sustainable farming practices.

3. Sorting & Grading: Post-harvest, the chili segmentation model can be used by food processing industries to sort and grade chili peppers based on visual features such as color and size. This can facilitate better quality control and help meet specific customer or market demands.

4. Pests and Disease Detection: The chili segmentation model can help identify abnormalities in leaf-chili and fruit-chili growth patterns, which could indicate the presence of pests or diseases. Early detection and targeted treatment can lead to a reduction in crop loss and higher yields.

5. Food Industry Applications: The model can be used for managing inventory and quality control in supermarkets, restaurants, and other food-related businesses. By identifying and categorizing different types of chili peppers, it can enable more efficient stock management and ensure the quality of products for customers.

Here are a few use cases for this project:

1. Traffic Management: This model could be used by traffic management authorities to analyze current road patterns and help optimize traffic flow. They could identify roads and their types in different areas, which would be crucial for implementing traffic signals, determining road capacities and planning traffic routes.

2. Autonomous Vehicles: Autonomous vehicles use computer vision technology constantly, key among them being the recognition of paths and roadways. With this image segmentation model, self-driving cars could accurately identify left roads, right roads, and U-turn roads, enhancing their navigation capacities and safety.

3. City Planning: Urban and city planners could use this technology to assess the infrastructure of both existing and developing cities. By identifying the types and layouts of roads, they can make better decisions about zoning, infrastructure development and renovation.

4. Virtual Map Development: Companies like Google, Apple, and Microsoft, which provide map services, could leverage the model to provide more detailed and accurate road information on their maps. This can improve user navigation experience.

5. Traffic Simulation Games: Game developers could use this image segmentation model in the development of hyper-realistic traffic or city simulation games. It could be used to create virtual environments with realistic road structures.

Here are a few use cases for this project:

1. Aircraft Maintenance and Safety Checks: This computer vision model could be used for conducting routine inspections of aircrafts for potential damages such as dents, scratches, or missing bolts. It could help eliminate human error and allow for much more detailed and accurate assessments.

2. Auto Repair and Inspection: The model could come in handy in auto repair shops to automate the process of identifying specific car damages. It can be employed for both pre and post-service inspection, ensuring that all missing bolts are replaced and dents repaired.

3. Quality Assurance in Painting Industries: Industries that deal with painting can apply this model to ensure quality in their painting process. The vision model can detect any paint-off from the object, notifying potential flaws that need correcting.

4. Automated Industrial Inspection: This model could be utilized in various industries during production, ensuring machines, and equipment are properly installed and not damaged. This could help mitigate potential accidents or operational disruptions in the manufacturing line.

5. Railway and Infrastructure Maintenance: The model can have its use in maintaining the safety of bridges, railways, and other constructed infrastructure by identifying any cracks, scratches or wear. This preemptive measure might prevent potential catastrophes caused by such infrastructure failures.

Here are a few use cases for this project:

1. 'Mask Compliance Monitoring': By using the Lips Segmentation model, businesses can track whether their employees are appropriately wearing masks in the workplace, potentially feeding into public health initiatives for pandemic control.

2. 'Cosmetic Testing and Augmentation': The system could be used in beauty apps or virtual platforms which allow users to experiment with different shades of lipstick or other lip cosmetics. It can display accurate virtual results on the user's lips, taking into account dynamic elements such as wrinkles or hair around the lip area.

3. 'Virtual Reality and Game Development': This model can help designers create more realistic characters by accurately mapping and detecting lip features, including the complex effects of aging, like wrinkles. It can be further used to improve lip-syncing of characters according to the dialogues and expressions.

4. 'Medical Diagnostics Aid': The 'Lips Segmentation' model can be used in the medical field for diagnostics and treatment planning for cases involving oral diseases, facial reconstructions, or maxillofacial surgeries.

5. 'Video Conferencing Enhancement': For better video chat conferencing experiences, the model could provide real-time filters, like blurring the background but keeping the user's face, namely the lips, in focus. This would be of interest in professional or social virtual meetings.

Image data described by high-level numeric-valued attributes, 7 classes# Source: Creators: Vision Group, University of Massachusetts Donor: Vision Group (Carla Brodley, brodley '@' cs.umass.edu)

Data Set Information:

The instances were drawn randomly from a database of 7 outdoor images. The images were handsegmented to create a classification for every pixel. Each instance is a 3x3 region.

Attribute Information:

1. region-centroid-col: the column of the center pixel of the region. 2. region-centroid-row: the row of the center pixel of the region. 3. region-pixel-count: the number of pixels in a region = 9. 4. short-line-density-5: the results of a line extractoin algorithm that counts how many lines of length 5 (any orientation) with low contrast, less than or equal to 5, go through the region. 5. short-line-density-2: same as short-line-density-5 but counts lines of high contrast, greater than 5. 6. vedge-mean: measure the contrast of horizontally adjacent pixels in the region. There are 6, the mean and standard deviation are given. This attribute is used as a vertical edge detector. 7. vegde-sd: (see 6) 8. hedge-mean: measures the contrast of vertically adjacent pixels. Used for horizontal line detection. 9. hedge-sd: (see 8). 10. intensity-mean: the average over the region of (R + G + B)/3 11. rawred-mean: the average over the region of the R value. 12. rawblue-mean: the average over the region of the B value. 13. rawgreen-mean: the average over the region of the G value. 14. exred-mean: measure the excess red: (2R - (G + B)) 15. exblue-mean: measure the excess blue: (2B - (G + R)) 16. exgreen-mean: measure the excess green: (2G - (R + B)) 17. value-mean: 3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics) 18. saturatoin-mean: (see 17) 19. hue-mean: (see 17) # Relevant Papers: N/A # Papers That Cite This Data Set1: Anthony K H Tung and Xin Xu and Beng Chin Ooi. CURLER: Finding and Visualizing Nonlinear Correlated Clusters. SIGMOD Conference. 2005.
2. Xiaoli Z. Fern and Carla Brodley. Cluster Ensembles for High Dimensional Clustering: An Empirical Study. Journal of Machine Learning Research n, a. 2004.
3. Aristidis Likas and Nikos A. Vlassis and Jakob J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, 36. 2003.
4. Manoranjan Dash and Huan Liu and Peter Scheuermann and Kian-Lee Tan. Fast hierarchical clustering and its validation. Data Knowl. Eng, 44. 2003.
5. Adil M. Bagirov and John Yearwood. A new nonsmooth optimization algorithm for clustering. Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, University of Ballarat.
6. K. A. J Doherty and Rolf Adams and Neil Davey. Non-Euclidean Norms and Data Normalisation. Department of Computer Science, University of Hertfordshire, College Lane.
7. Michael Lindenbaum and Shaul Markovitch and Dmitry Rusakov. Selective Sampling Using Random Field Modelling.
8. James Tin and Yau Kwok. Moderating the Outputs of Support Vector Machine Classifiers. Department of Computer Science Hong Kong Baptist University Hong Kong.
9. Thomas T. Osugi and M. S. EXPLORATION-BASED ACTIVE MACHINE LEARNING. Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements.
10. Nikos A. Vlassis and Aristidis Likas. A greedy EM algorithm for Gaussian mixture. Intelligent Autonomous Systems, IAS.
11. Amund Tveit. Empirical Comparison of Accuracy and Performance for the MIPSVM classifier with Existing Classifiers. Division of Intelligent Systems Department of Computer and Information Science, Norwegian University of Science and Technology.
12. Je Scott and Mahesan Niranjan and Richard W. Prager. Realisable Classifiers: Improving Operating Performance on Variable Cost Problems. Cambridge University Department of Engineering.
13. C. Titus Brown and Harry W. Bullen and Sean P. Kelly and Robert K. Xiao and Steven G. Satterfield and John G. Hagedorn and Judith E. Devaney. Visualization and Data Mining in an 3D Immersive Environment: Summer Project 2003.
14. Adil M. Bagirov and Alex Rubinov and A. N. Soukhojak and John Yearwood. Unsupervised and supervised data classification via nonsmooth and global optimization. School of Information Technology and Mathematical Sciences, The University of Ballarat.
15. K. A. J Doherty and Rolf Adams and Neil Davey. Unsupervised Learning with Normalised Data and Non-Euclidean Norms. University of Hertfordshire.

Citation Request:

Please refer to the Machine Learning Repository's citation policy. [1] Papers were automatically harvested and associated with this data set, in collaborationwith Rexa.info

Source: http://archive.ics.uci.edu/ml/datasets/Image+Segmentation

This is an Instance Segmentation project for visualizing detected cracks on concrete. This dataset is usable for those doing transportation and public safety studies, creating self-driving car models, or testing out computer vision models for fun. * Featured Transportation Projects

Check out this example guide from Augmented Startups to see the model in action and learn how the dataset came together: https://medium.com/augmented-startups/yolov7-segmentation-on-crack-using-roboflow-dataset-f13ae81b9958

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