Import images in Supervisely format
Images with corresponding annotations
Drag and drop images to Supervisely, supported formats: .jpg, .jpeg, jpe, .mpo, .bmp, .png, .tiff, .tif, .webp, .nrrd
This app allows you to upload only images without any annotations.
By default, flags "normalize EXIF" and "remove alpha channel" are disabled.
⚠️ Be aware that "remove files after successful import" flag is enabled by default, it will automatically remove source directory after import.
Supported image formats: .jpg
, .jpeg
, jpe
, .bmp
, .png
, .webp
, .mpo
, .tiff
, .nrrd
, .jfif
, .avif
, .heic
.
Single image file size limits:
Community
plan: 25MBPro
plan: 300MBEnterprise
edition: no limits⚠️ Images in .nrrd
format can be viewed in Image annotation tool v2 only.
🗄️ 1.2.22
Starting from this version application supports uploading files from a single archive. To do so, change the context menu to the File.
🏋️ 1.2.7
Starting from this version application supports import from the special directory on your local computer. It is made for Enterprise Edition customers who need to upload tens or even hundreds of gigabytes of data without using a drag-and-drop mechanism:
Team Files
→ Supervisely Agent
and find your folder there.🔥 1.2.0
Starting from this version application automatically compares image file extension with actual image MIME type and corrects extension if needed. For example: if you import image my_image.png
, but it is actually a TIFF then the image will be automatically renamed to my_image.tiff
.
🖼️ 1.2.29
Starting from this version added support for .jfif
format.
🖼️ 1.2.31
Starting from this version you can upload .avif
and .heic
formats (will be converted to .jpg
). Additionally, fixed case sensitivity issues for file extensions.
💡 You can download the archive with data example here.
Team Files
Subdirectories inside the root directory (the one that you run the app from or select in the team files selector when starting the app from the ecosystem) define dataset names. Images in the root directory will be moved to a dataset with the name "ds0
".
.
📁my_images_project
├── 🖼️img_01.jpeg
├── ...
├── 🖼️img_09.png
├── 📁my_folder1
│ ├── 🖼️img_01.JPG
│ ├── 🖼️img_02.jpeg
│ └── 📁my_folder2
│ ├── 🖼️img_13.jpeg
│ ├── ...
│ └── 🖼️img_9999.png
└── 📁my_folder3
├── 🖼️img_01.JPG
├── 🖼️img_02.jpeg
└── 🖼️img_03.png
As a result, we will get a project with 3 datasets with the names: ds0
, my_folder1
, and my_folder3
. Dataset my_folder1
will also contain images from my_folder2
directory.
Drag & Drop
Think of a drag-and-drop area as the root directory for your datasets that is empty for now. Drop multiple folders with images into the drag & drop area. Directories that you drop inside the drag-and-drop area are defined as datasets. If you drag & drop images without a folder, these images will be moved to the dataset with the name "ds0
".
├── 🖼️img_01.jpeg
├── ...
├── 🖼️img_09.png
├── 📁my_folder1
│ ├── 🖼️img_01.JPG
│ ├── 🖼️img_02.jpeg
│ └── 📁my_folder2
│ ├── 🖼️img_13.jpeg
│ ├── ...
│ └── 🖼️img_9999.png
└── 📁my_folder3
├── 🖼️img_01.JPG
├── 🖼️img_02.jpeg
└── 🖼️img_03.png
As a result we will get project with 3 datasets with the names: ds0
, my_folder1
, my_folder3
. Dataset my_folder1
will also contain images from my_folder2
directory.
The app can be launched from the ecosystem, team files, images project and images dataset
Step 1. Run the app from Ecosystem
Step 2. Drag & drop folder or images files, select options and press the Run button
Step 1. Run the application from the context menu of the directory with images on Team Files page
Step 2. Select options and press the Run button
Step 1. Run the application from the context menu of the Images Project
Step 2. Drag & drop folder or images files, select options and press the Run button
Step 1. Run the application from the context menu of the Images Dataset
Step 2. Drag & drop folder or images files, select options and press the Run button
Example of uploading a flat set of images to Team Files:
Images with corresponding annotations
Transform YOLO v5 format to supervisely project
Upload images using .CSV file
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Converts COCO format to Supervisely