Image Segmentation Data Labelling - Damage Area and Vehicle Part Annotation
Budget: ₹1,500 – ₹12,500 INR
We are seeking skilled data labellers to assist us with an image segmentation project. The task involves accurately labelling two types of segments in each image.
Type 1: Damage Area Segmentation (Polygon):
In this type, labellers will be responsible for accurately outlining the damaged areas on the provided images. The damaged area is represented by a polygon shape. It is crucial to precisely mark the boundaries of the damage to ensure accurate segmentation.
Type 2: Vehicle Part Annotation (Multiple Classes):
Labellers will also be required to annotate the specific part of the vehicle where the damage occurred. This type can involve various classes, ranging from six or more, depending on the complexity of the damage. It is essential to assign the correct class label to each part accurately.
Applicants should possess the following qualifications:
- Proficient understanding of image segmentation techniques
- Strong attention to detail and ability to precisely annotate boundaries
- Familiarity with polygon annotation tools
- Experience in labelling tasks involving multiple classes
Please note that more detailed instructions will be provided once we commence discussions.
To be considered for this position, please provide samples of your previous image segmentation and annotation work. This will help us evaluate your skills and ensure a high level of accuracy in your labelling.
If you are passionate about image annotation, have a keen eye for detail, and are dedicated to delivering high-quality results, we encourage you to apply. We value reliability, professionalism, and a commitment to meeting deadlines.
This is a freelance position, and compensation will be based on the number of images labelled accurately. The project offers an excellent opportunity to work on interesting and challenging segmentation tasks.
To apply, please submit your portfolio or relevant work samples along with your proposal. We look forward to collaborating with talented data labellers who share our commitment to excellence.
Feel free to ask any questions you may have, and we will be happy to provide further information during our discussions.
Familiarity with popular image annotation tools such as Labelbox, RectLabel, VGG Image Annotator (VIA), CVAT (Computer Vision Annotation Tool), or similar platforms is valuable. Experience in using these tools for accurate annotation and segmentation will streamline the labelling process.
Type 1: Damage Area Segmentation (Polygon):
In this type, labellers will be responsible for accurately outlining the damaged areas on the provided images. The damaged area is represented by a polygon shape. It is crucial to precisely mark the boundaries of the damage to ensure accurate segmentation.
Type 2: Vehicle Part Annotation (Multiple Classes):
Labellers will also be required to annotate the specific part of the vehicle where the damage occurred. This type can involve various classes, ranging from six or more, depending on the complexity of the damage. It is essential to assign the correct class label to each part accurately.
Applicants should possess the following qualifications:
- Proficient understanding of image segmentation techniques
- Strong attention to detail and ability to precisely annotate boundaries
- Familiarity with polygon annotation tools
- Experience in labelling tasks involving multiple classes
Please note that more detailed instructions will be provided once we commence discussions.
To be considered for this position, please provide samples of your previous image segmentation and annotation work. This will help us evaluate your skills and ensure a high level of accuracy in your labelling.
If you are passionate about image annotation, have a keen eye for detail, and are dedicated to delivering high-quality results, we encourage you to apply. We value reliability, professionalism, and a commitment to meeting deadlines.
This is a freelance position, and compensation will be based on the number of images labelled accurately. The project offers an excellent opportunity to work on interesting and challenging segmentation tasks.
To apply, please submit your portfolio or relevant work samples along with your proposal. We look forward to collaborating with talented data labellers who share our commitment to excellence.
Feel free to ask any questions you may have, and we will be happy to provide further information during our discussions.
Familiarity with popular image annotation tools such as Labelbox, RectLabel, VGG Image Annotator (VIA), CVAT (Computer Vision Annotation Tool), or similar platforms is valuable. Experience in using these tools for accurate annotation and segmentation will streamline the labelling process.