Epigos offers an API that allows you to upload dataset to your projects. This API is accessible within the Epigos Python SDK.

You’ll need to Create an API Key to Authentication with Epigos AI.

Managing Projects

To manage projects, you can use the Project class:

project()

Retrieves a project from Epigos AI and returns the Project class that can be used to upload datasets.

# Load project
project = client.project("project_id")
print(project)

Upload dataset

You have the option to upload images alongside annotations suitable for tasks such as object detection and classification.

upload()

You can upload a single image along with its annotation to the project. Currently supported annotation formats are Pascal VOC and YOLO.

Param	Type	Description
`image_path`	string	Path or directory to images to upload.
`annotation_path`	string	Optional path to annotation file to annotate the image. For image classification, this will be used as class label if `use_folder_as_class_name` is false.
`batch_name`	string	Name of batch to upload to within project. Defaults to `sdk-upload`.
`box_format`	string	Format of annotation to upload. Options are (`coco`, `pascal_voc`, `yolo`). Defaults to `pascal_voc`.
`labels_map`	dict	Optional Epigos AI label ID to class name mapping. Useful to skip creating labels if it’s already created in Epigos AI.
`yolo_labels_map`	dict	Optional class ID to label name mapping for YOLO annotations. Used to map numeric YOLO label ID’s to actual class names.
`batch_id`	string	Optional batch ID if batch is already created in Epigos AI. If not provided, `batch_name` is used to create/retrieve a batch from Epigos AI.
`use_folder_as_class_name`	bool	Optional flag to indicate whether to use parent folder name of image is class label for image classification dataset.

Upload file for image classification

record = project.upload(
  "path/to/image.jpg",
  use_folder_as_class_name=True,
)
print(record)

Upload image with COCO annotation

record = project.upload(
  "path/to/image.jpg",
  annotation_path="path/to/coco.json",
  box_format="coco"
)
print(record)

Upload image with Pascal VOC annotation

record = project.upload(
  "path/to/image.jpg",
  annotation_path="path/to/image.xml",
  box_format="pascal_voc"
)
print(record)

Upload image with YOLO annotation

record = project.upload(
  "path/to/image.jpg",
  annotation_path="path/to/image.txt",
  box_format="yolo"
)
print(record)

upload_classification_dataset()

You can upload an entire image classification dataset folder. This is useful when you have multiple images in a directory. The parent folder of the images will be used as categories for the image. Below is a sample of how the directory should look like.

root/dog/xxx.jpg
root/dog/xxy.jpg
root/dog/[...]/xxz.jpg

root/cat/123.jpg
root/cat/nsdf3.jpg
root/cat/[...]/asd932_.jpg

Param	Type	Description
`images_directory`	string	Path to directory containing images to upload.
`batch_name`	string	Name of batch to upload to within project. Defaults to `sdk-upload`.
`num_workers`	integer	Number of cpu workers to use for uploading. Defaults to `4`

records = project.upload_classification_dataset(
  "path/to/folder",
)
print(tuple(records))

upload_coco_dataset()

You can upload an entire folder containing COCO annotations and images. This is useful when you have multiple images in a directory.

Param	Type	Description
`images_directory`	string	Path to directory containing images to upload.
`annotations_path`	string	Path to file containing COCO annotations JSON file.
`batch_name`	string	Name of batch to upload to within project. Defaults to `sdk-upload`.
`labels_map`	dict	Optional Epigos AI label ID to class name mapping. Useful to skip creating labels if it’s already created in Epigos AI.
`num_workers`	integer	Number of cpu workers to use for uploading. Defaults to `4`

records = project.upload_coco_dataset(
  images_directory="path/to/dataset/train/images",
  annotations_path="path/to/dataset/train/coco.json",
)
print(tuple(records))

upload_pascal_voc_dataset()

You can upload an entire folder containing PASCAL VOC annotations and images. This is useful when you have multiple images in a directory.

Param	Type	Description
`images_directory`	string	Path to directory containing images to upload.
`annotations_directory`	string	Path to directory containing PASCAL VOC annotations.
`batch_name`	string	Name of batch to upload to within project. Defaults to `sdk-upload`.
`labels_map`	dict	Optional Epigos AI label ID to class name mapping. Useful to skip creating labels if it’s already created in Epigos AI.
`num_workers`	integer	Number of cpu workers to use for uploading. Defaults to `4`

records = project.upload_pascal_voc_dataset(
  images_directory="path/to/dataset/train/images",
  annotations_directory="path/to/dataset/train/labels",
)
print(tuple(records))

upload_yolo_dataset()

You can upload an entire folder containing YOLO annotations and images. This is useful when you have multiple images in a directory.

Param	Type	Description
`images_directory`	string	Path to directory containing images to upload.
`annotations_directory`	string	Path to directory containing YOLO annotations.
`data_yaml_path`	string	Path to file containing YOLO data configuration file yaml.
`batch_name`	string	Name of batch to upload to within project. Defaults to `sdk-upload`.
`labels_map`	dict	Optional Epigos AI label ID to class name mapping. Useful to skip creating labels if it’s already created in Epigos AI.
`num_workers`	integer	Number of cpu workers to use for uploading. Defaults to `4`

records = project.upload_yolo_dataset(
  images_directory="path/to/dataset/train/images",
  annotations_directory="path/to/dataset/train/labels",
  data_yaml_path="path/to/dataset/data.yaml",
)
print(tuple(records))

Checkout the code on Github

SDKs

SDKs

Project

Managing Projects

project()

Upload dataset

upload()

Upload file for image classification

Upload image with COCO annotation

Upload image with Pascal VOC annotation

Upload image with YOLO annotation

upload_classification_dataset()

upload_coco_dataset()

upload_pascal_voc_dataset()

upload_yolo_dataset()

​Managing Projects

​project()

​Upload dataset

​upload()

​Upload file for image classification

​Upload image with COCO annotation

​Upload image with Pascal VOC annotation

​Upload image with YOLO annotation

​upload_classification_dataset()

​upload_coco_dataset()

​upload_pascal_voc_dataset()

​upload_yolo_dataset()

Managing Projects

project()

Upload dataset

upload()

Upload file for image classification

Upload image with COCO annotation

Upload image with Pascal VOC annotation

Upload image with YOLO annotation

upload_classification_dataset()

upload_coco_dataset()

upload_pascal_voc_dataset()

upload_yolo_dataset()