Releases: BelickNicko/AutoCVAT
v1.2.2
Creating automatic annotation in CVAT using pre-trained ultralytics models. It supports both bounding box annotation for object detection tasks and polygon annotation for instance segmentation tasks.
You need to provide the path to the folder with images that will be uploaded to the task in CVAT. As a result of the program's operation, a zip archive with annotations obtained based on the neural network inference will be formed.
All that remains to be done: after creating the task, go to the Actions panel and select Upload annotations. Then, choose COCO 1.0 in the Import format section and upload the archive.
🚀MAIN UPDATES:
The Zero-Shot mode for instance segmentation was significantly accelerated.
v1.2.1
Creating automatic annotation in CVAT using pre-trained [ultralytics models][1]. It supports both bounding box annotation for object detection tasks and polygon annotation for instance segmentation tasks.
You need to provide the path to the folder with images that will be uploaded to the task in CVAT. As a result of the program's operation, a zip archive with annotations obtained based on the neural network inference will be formed.
All that remains to be done: after creating the task, go to the Actions panel and select Upload annotations. Then, choose COCO 1.0 in the Import format section and upload the archive.
🚀MAIN UPDATES:
Fixed bugs and shortened the list of required libraries for installation
v1.2.0
Creating automatic annotation in CVAT using pre-trained [ultralytics models][1]. It supports both bounding box annotation for object detection tasks and polygon annotation for instance segmentation tasks.
You need to provide the path to the folder with images that will be uploaded to the task in CVAT. As a result of the program's operation, a zip archive with annotations obtained based on the neural network inference will be formed.
All that remains to be done: after creating the task, go to the Actions panel and select Upload annotations. Then, choose COCO 1.0 in the Import format section and upload the archive.
🚀MAIN UPDATES:
Now available: Zero-shot instance segmentation + you can turn any of your own detection neural networks into an instance segmentation network using the built-in box processing through FastSAM.
v1.1.0
Creating automatic annotation in CVAT using pre-trained [ultralytics models][1]. It supports both bounding box annotation for object detection tasks and polygon annotation for instance segmentation tasks.
You need to provide the path to the folder with images that will be uploaded to the task in CVAT. As a result of the program's operation, a zip archive with annotations obtained based on the neural network inference will be formed.
All that remains to be done: after creating the task, go to the Actions panel and select Upload annotations. Then, choose COCO 1.0 in the Import format section and upload the archive.
