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The Create ML JSON file is imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
RectLabel can import from "imagefilename" and "annotation" keys, too.
Export COCO JSON file
Specify the split ratio "80/10/10" so that all images are split into train, validation, and test sets.
When the shuffle checkbox is ON, images are randomly shuffled everytime you export. When the shuffle checkbox is OFF, images are taken from the current sort according to the split ratio.
Annotation files are exported as an COCO JSON file. Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms.
For a box object, "segmentation" is exported as empty.
For a keypoints object, "keypoints" and "num_keypoints" are exported.
You can export a keypoints object combined with a polygon object when you aligned the keypoints object at the row and the polygon object at the row + 1 on the label table.
You can export a keypoints object combined with a cubic bezier object or a pixels object when you aligned the keypoints object at the row and the cubic bezier object or the pixels object at the row + 1 on the label table.
The COCO JSON file is imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
Import COCO JSON per image files
You can import the COCO RLE JSON files of the SA-1B dataset.
This COCO format does not include the "category_id" so that each label name is set from the first element of the label name history.
Before importing, be sure that you opened images folder and annotations folder.
The Labelme JSON files are imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
Export YOLO txt files
Annotation files are exported in the YOLO text format.
A YOLO text file is saved per an image.
For a box object, the bounding box is saved.
Where center_x, center_y, width, and height are float values relative to width and height of the image.
For a rotated box, polygon, cubic bezier, line, point, and pixels object, the points coordinates are saved.
This format is for YOLOv5 and YOLOv8 Instance Segmentation.
The YOLO text files are imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
The DOTA text files are imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
Export CSV file
Annotation files are exported as an CSV file.
To train a Turi Create Object Detection model, select "image" for each line and check on the "Convert to boxes" checkbox.
(x, y) means the center of the box where (0, 0) is the top-left corner.
The CSV file is imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
Export train/val/test folders
Specify the split ratio "80/10/10" so that all images are split into train, validation, and test sets.
When the shuffle checkbox is ON, images are randomly shuffled everytime you export. When the shuffle checkbox is OFF, images are taken from the current sort according to the split ratio.
You can export train/val/test folders and the yaml file at once in the YOLO format or PASCAL VOC XML format. This exported folder can be uploaded to Roboflow directly.
Export train/val/test.txt files
Specify the split ratio "80/10/10" so that all images are split into train, validation, and test sets.
When the shuffle checkbox is ON, images are randomly shuffled everytime you export. When the shuffle checkbox is OFF, images are taken from the current sort according to the split ratio.
In the specified folder, train.txt, val.txt, and test.txt are saved.
sneakers-1.jpg
sneakers-2.jpg
...
Using "Full path" option, you can save full paths. Or you can add prefix to file names.
The object names file is created from the objects table on the settings dialog. YOLOv5 and YOLOv8 yaml file as dictionary.
The "flip_idx" array is to flip the "left" included keypoint position and the "right" included keypoint position.
Export an image includes all objects: An indexed color image which includes all objects is saved as {image_file_name}_all_objects.png.
Export an image per object class: A grayscale image per object class is saved as {image_file_name}class{class_name}.png.
Export an image per object: A grayscale image per object is saved as {image_file_name}_object{object_idx}.png.
For the indexed color image, overlaps of objects are based on the layer order on the label table.
Pixel values are set based on the object index on the objects table and 0 is set for the background.
The indexed color table is created from object colors on the objects table.
For grayscale images, pixel values are set 255 for the foreground and 0 for the background.
Export screenshots
You can export images and annotations as jpg images.
It exports labels when showing labels on boxes and exports coordinates when showing coordinates on boxes.
Export augmented images
Images and annotations are augmented using "Flip", "Crop", "Contrast", and "Rotate".
For "Flip", each image is flipped horizontally with 0.5 probability.
For "Crop", each image is cropped to [100% - value, 100%] of the original size.
For "Contrast", each image contrast is changed to [100% - value, 100% + value].
For "Rotate", each image is rotated to [-value, value] degrees.
For "Number of augmented images", the number of generated images from an image through the augmentation.
If the object is cut out so that the bounding box size is less than 0.01 of the original size, the object is removed.
To flip keypoints horizontally, use "left" and "right" prefix or suffix for each keypoint name.
Export sliced images
Images and annotations are sliced horizontally and vertically.
For "Horizontal slices", each image is sliced horizontally by the number of horizontal slices.
For "Vertical slices", each image is sliced vertically by the number of vertical slices.
Export Create ML JSON file
Annotation files are exported as an Create ML JSON file.
Put training images and the JSON file into the same folder.
Do not put any other files in the folder and be sure that image file names do not contain spaces.
Import Create ML JSON file
The Create ML JSON file is imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
RectLabel can import from "imagefilename" and "annotation" keys, too.
Export COCO JSON file
Specify the split ratio "80/10/10" so that all images are split into train, validation, and test sets.
When the shuffle checkbox is ON, images are randomly shuffled everytime you export. When the shuffle checkbox is OFF, images are taken from the current sort according to the split ratio.
Annotation files are exported as an COCO JSON file.
Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms.
For a box object, "segmentation" is exported as empty.
For a rotated box, polygon, line, and point object, "segmentation" is exported as polygons.
For a pixels object, "segmentation" is exported as RLE.
RLE is encoding the mask image using the COCO Mask API.
For a keypoints object, "keypoints" and "num_keypoints" are exported.
You can export a keypoints object combined with a polygon object when you aligned the keypoints object at the row and the polygon object at the row + 1 on the label table.
You can export a keypoints object combined with a cubic bezier object or a pixels object when you aligned the keypoints object at the row and the cubic bezier object or the pixels object at the row + 1 on the label table.
In "categories", "keypoints" and "skeleton" are exported.
Import COCO JSON file
The COCO JSON file is imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
Import COCO JSON per image files
You can import the COCO RLE JSON files of the SA-1B dataset.
This COCO format does not include the "category_id" so that each label name is set from the first element of the label name history.
Before importing, be sure that you opened images folder and annotations folder.
Export Labelme JSON files
Annotation files are exported as Labelme JSON files.
Import Labelme JSON files
The Labelme JSON files are imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
Export YOLO txt files
Annotation files are exported in the YOLO text format.
A YOLO text file is saved per an image.
For a box object, the bounding box is saved.
Where center_x, center_y, width, and height are float values relative to width and height of the image.
For a rotated box, polygon, cubic bezier, line, point, and pixels object, the points coordinates are saved.
This format is for YOLOv5 and YOLOv8 Instance Segmentation.
For a keypoints object, the bounding box and the points coordinates are saved.
This format is for YOLOv8 and YOLO-Pose.
Import YOLO txt files
The YOLO text files are imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
Export DOTA txt files
Annotation files are exported in the DOTA oriented bounding box (OBB) text format.
This format is for Yolov5 for Oriented Object Detection, MMRotate, and YOLOv8 OBB.
Import DOTA txt files
The DOTA text files are imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
Export CSV file
Annotation files are exported as an CSV file.
To train a Turi Create Object Detection model, select "image" for each line and check on the "Convert to boxes" checkbox.
(x, y) means the center of the box where (0, 0) is the top-left corner.
When you select "image" for each line and check off the "Convert to boxes" checkbox.
When you select "label" for each line and check on the "Convert to boxes" checkbox.
When you select "label" for each line and check off the "Convert to boxes" checkbox.
Import CSV file
The CSV file is imported to annotation files in the current folder.
Before importing, be sure that you opened images folder and annotations folder.
Export train/val/test folders
Specify the split ratio "80/10/10" so that all images are split into train, validation, and test sets.
When the shuffle checkbox is ON, images are randomly shuffled everytime you export. When the shuffle checkbox is OFF, images are taken from the current sort according to the split ratio.
You can export train/val/test folders and the yaml file at once in the YOLO format or PASCAL VOC XML format. This exported folder can be uploaded to Roboflow directly.
Export train/val/test.txt files
Specify the split ratio "80/10/10" so that all images are split into train, validation, and test sets.
When the shuffle checkbox is ON, images are randomly shuffled everytime you export. When the shuffle checkbox is OFF, images are taken from the current sort according to the split ratio.
In the specified folder, train.txt, val.txt, and test.txt are saved.
Using "Full path" option, you can save full paths. Or you can add prefix to file names.
Export object names file
The object names file is created from the objects table on the settings dialog.
YOLOv5 and YOLOv8 yaml file as dictionary.
The "flip_idx" array is to flip the "left" included keypoint position and the "right" included keypoint position.
YOLOv5 yaml file as array.
Object names text file.
Tensorflow Object Detection API label map file.
Import object names file
You can import an object names file or import object names from xml files.
Export mask images
The mask images are exported in the PNG format.
Run an instance segmentation model on Tensorflow Object Detection API.
You can specify which mask image to export.
For the indexed color image, overlaps of objects are based on the layer order on the label table.
Pixel values are set based on the object index on the objects table and 0 is set for the background.
The indexed color table is created from object colors on the objects table.
For grayscale images, pixel values are set 255 for the foreground and 0 for the background.
Export screenshots
You can export images and annotations as jpg images.
It exports labels when showing labels on boxes and exports coordinates when showing coordinates on boxes.
Export augmented images
Images and annotations are augmented using "Flip", "Crop", "Contrast", and "Rotate".
For "Flip", each image is flipped horizontally with 0.5 probability.
For "Crop", each image is cropped to [100% - value, 100%] of the original size.
For "Contrast", each image contrast is changed to [100% - value, 100% + value].
For "Rotate", each image is rotated to [-value, value] degrees.
For "Number of augmented images", the number of generated images from an image through the augmentation.
If the object is cut out so that the bounding box size is less than 0.01 of the original size, the object is removed.
To flip keypoints horizontally, use "left" and "right" prefix or suffix for each keypoint name.
Export sliced images
Images and annotations are sliced horizontally and vertically.
For "Horizontal slices", each image is sliced horizontally by the number of horizontal slices.
For "Vertical slices", each image is sliced vertically by the number of vertical slices.
Export images for classification
All images are exported into object-named subfolders.
Creating an Image Classifier Model on Create ML.
Export objects and attributes stats
The number of used objects is saved as objects_stats.txt file.
The number of used attributes is saved as attributes_stats.txt file.
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