Labelling image dataset for vehicle's body damage detection - Part 2

We want to label a second batch of 5000 images with a budget of approximately 500€.

The dataset is oriented to detect external surface failures of vehicle's body (never inner). It contains a subset of 13 categories of possible defects on a car. Most of them happen on collision.

Labelling instructions: --------------------------------------------------------------------------------------------

In this task, you will use a box tool to draw a tight rectangle around each defect on a vehicle in the images.

 Make sure to use only one rectangle for each defect

 Make sure to draw rectangles VERY tight in size respect to the defects

 Mark the defect even if it is partially occluded

 Take into account the priority list when there is an overlapping between them

 If a vehicle is unclear or less than 10x10 pixels do not mark it

 If there are no vehicles or there is no defect to box, you must click 'Nothing to box'


When labelling, the following order of priority should be followed. The idea is to use different labels for defects that are in different zones. Defects in the same area should be labelled in this order of priority:

- Break

- Perforation

- Wheel Damage

- Tire Damage

- Dent

- Burn

- Crack Glass

- Missing Part

- Stripe

- Scratch

- Impact Glass

- Small impact

- Extremely Dirty

That is, if there is a "break", you do not need to label the "scratches" and "dents" associated with that "break." Or if there is a "dent" along with "stripe", simply label it as "dent" and the bounding box delimits the entire area affected by the defect.

Required output: ----------------------------------------------------------------------------------------------------

The output should be presented in COCO format.

The final output to submit should be a JSON file where all image ids are unique from 0-9999 (in case this task is done by different people all their labels should be reconditioned to fulfil this requirement).

Category examples: ----------------------------------------------------------------------------------------------

To describe better the categories, please find attached a couple of images on each category.

Aptitudini: Procesare date, Machine Learning (ML), Informatică, Computer Vision, Deep Learning

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Despre angajator:
( 1 părere ) Donostia / San Sebastian, Spain

ID Proiect: #29439699

Acordat lui:


Hi there, I have placed my bid with the same amount & days to complete it like last time. Thanks & Regards, Jyoti Joshi

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15 freelanceri licitează în medie 545€ pentru acest proiect

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Hi There, I woud like to see some unprocessed sample images. After that I will be able to tell the exact budget and time for job completion. Thanks Satish

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Hi There, I use RoboFlow to lable images. Done similar job last week for labeling corns of 20 different categories. Please share 10 images so I can submit you sample output in coco format for your review. Available Mai multe

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Greetings! Sir I am a professional Computer vision expert having experience in Deep learning and Image processing. I have done several projects for the industry. I can do any type of annotations as I have prior experie Mai multe

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Hey ?? ! I have had labelling experience in the past. I am meticulously read your requirements and I can assure you of the great work with precision. I had labelled some metal losses in an iron image in the past for co Mai multe

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Thanks for the Opportunity, - I am mechanical engineer doing data entry works here... That's how i interested in this job - I have gone through your PDF 13 categories of possible defects - In this task, I have to draw Mai multe

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Hi, I have seen your attached pdf file.I can deliver you 5000 images data set in your required format.I can labelling the images with red [login to view URL] hire, you can test by only 5£ for 15 images. Thanks

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Hello, I have gone through your project requirements and it seems like you need someone to highlight the external damage on cars (NOT INTERNAL) and make a rectangle around it. I have read all the requirements and am w Mai multe

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I am a Data Design Engineer, Data Annotator CONVSYS (PVT) LTD. Responsibilities: Data Collection, Data Analysis, Data Cleaning ,Data Annotations, Model Training and Testing, Image Classifications, Object Tracking Proj Mai multe

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Hi there, I’m well versed in labelling image for AI/ML model and I'm a developer too. I know this step really needs the high accuracy of each image of cars. I read your guide carefully and know how to do it. I’m the Mai multe

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Hi i am a Microsoft Specialist and i have a ability to make work like sky rocket so if you want a good work kindly dm me Respected sir

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I am engaging and well written without spelling or grammatical errors. I have the required skills & experience relate to the project

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Hi, I have experience in labeling images using the Labelme and LabelImg tools. Labeling 5000+ images is a consistent task that requires a lot of effort, perseverance, more time but most important of all concentration b Mai multe

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