Import fiftyone as fo import fiftyone.zoo as foz import as fouy dataset = foz. The data/ and labels/ files may contain nested subfolders of parallelly Unlabeled images have no corresponding file in labels/. When reading datasets of this type, all columns after the four bbox columns (optional) A float confidence for the detection Rotation around the y-axis in camera coordinates, in Observation angle of the object, in ĢD bounding box of object in the image in pixels, in theģD object dimensions, in meters, in the formatģD object location (x, y, z) in camera coordinates Where:- 0 = fully visible- 1 = partly occluded- 2 = Here, truncation refers to the objectĪn int in (0, 1, 2, 3) indicating occlusion state,
To an object and the 15 (and optional 16th score) columns have the followingĪ float in, where 0 is non-truncated andġ is fully truncated. Where the labels TXT files are space-delimited files where each row corresponds count ( "ground_tections" )) print ( dataset. count ( "ground_tections" )) print ( dataset2. add_coco_labels ( dataset2, "predictions", "/tmp/coco/predictions.json", classes, ) # Verify that ground truth and predictions were imported as expected print ( dataset. COCODetectionDataset, label_field = "ground_truth", ) # And add model predictions fouc. from_dir ( dataset_dir = "/tmp/coco", dataset_type = fo. COCODetectionDataset, labels_path = "/tmp/coco/predictions.json", label_field = "predictions", classes = classes, ) # Now load ground truth labels into a new dataset dataset2 = fo. COCODetectionDataset, label_field = "ground_truth", classes = classes, ) # Export predictions dataset. export ( export_dir = "/tmp/coco", dataset_type = fo. distinct ( "" ) # Export images and ground truth labels to disk dataset. load_zoo_dataset ( "quickstart" ) classes = dataset. Import fiftyone as fo import fiftyone.zoo as foz import as fouc dataset = foz. Path to the image in a nested subfolder of data/Īn absolute path to an image, which may or may not be in the data/ folder The filename of an image in the data/ folderĪ relative path like data/sub/folder/filename.ext specifying the relative The file_name attribute of the labels file encodes the location of theĬorresponding images, which can be any of the following: "name": "Attribution-NonCommercial-ShareAlike License",įor unlabeled datasets, labels.json does not contain an annotations field. Images in data/ should be arranged in nested subfolders with theĬorresponding names, or they can be absolute paths, in which case the images The UUIDs can also be relative paths like path/to/uuid, in which case the Unlabeled videos can have a None (or missing) key in labels. Provided, then the target values directly store the label strings. Mapped to class label strings via classes. If the classes field is provided, the target values are class IDs that are Or the timestamps key, which should contain the timestamps of Key, which should contain the frame numbers of the detection, The temporal range of each detection can be specified either via the support Import datasets in custom formats by defining your own Dataset or Saved in Berkeley DeepDrive (BDD) format.Īn image dataset whose image data and optional properties are stored inĪn image or video dataset whose location data and labels are stored inĪn image dataset whose image and geolocation data are stored inĪ labeled dataset consisting of videos and their associated multitask predictionsĪ dataset consisting of an entire serialized Dataset and its associated source Ī labeled dataset consisting of images and their associated semantic segmentationsĪ labeled dataset consisting of images and their associated multitask labelsĪ labeled dataset consisting of videos and their associated multitask labelsĪ labeled dataset consisting of images and their associated multitask predictions Stored as TFRecords in TF Object Detection API format. Refer to the corresponding dataset format when reading the dataset from disk.Ī labeled dataset consisting of images and their associated classification labelsĪ directory tree whose subfolders define an image classification dataset.Ī directory tree whose subfolders define a video classification dataset.Ī labeled dataset consisting of images and their associated object detectionsĪ labeled dataset consisting of videos and their associated temporal detections in Each supported dataset type is represented by a subclass ofį, which is used by the Python library and CLI to
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