I want to annotate custom objects in around 4.000 images, where each image contains many objects. I cannot accomplish the work by hand as you can understand. I searched on google and here on stackoverflow, but the solutions are based on "common" annotations let's say, such as car, horse, person, house, etc. I want to annotate custom datasets that they do not exist as "common"/"ready" in the platforms. How can I proceed?
I need to have polygons' labeling and not just rectangles on each automatically annotated object. And have the annotations in .json format. Any ideas?
I want to annotate custom objects in around 4.000 images, where each image contains many objects. I cannot accomplish the work by hand as you can understand. I searched on google and here on stackoverflow, but the solutions are based on "common" annotations let's say, such as car, horse, person, house, etc. I want to annotate custom datasets that they do not exist as "common"/"ready" in the platforms. How can I proceed?
I need to have polygons' labeling and not just rectangles on each automatically annotated object. And have the annotations in .json format. Any ideas?
Share Improve this question edited Nov 19, 2024 at 11:54 nobody asked Nov 18, 2024 at 13:13 nobodynobody 4653 gold badges12 silver badges26 bronze badges1 Answer
Reset to default 1I can suggest the following strategy to tackle the annotation task you want to complete:
- Use open source annotation platform like
cvatto annotate small batch first (500 images for example) - cvat
Train a detection model on the annotated images -> run inference on a second batch of images (let's say another 500) -> revisit model predictions. It should take less time to revisit predictions
Repeat 1 and 2 until you finish the data that you need to annotate.
Regarding the polygon requirement, I recommend to use a zero-shot segmentation model like
SAMwhere you prompt it with ground truth rectangles that you get from the annotation phase suggested above.
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