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Adding Data
Getting your images -- or videos! -- into Roboflow.
Data can currently be added to Object Detection and Classification projects only. For interest adding data for other project types requesting early access is highly encouraged. The sections below illustrate how to initialize Object Detection and Classification projects so they may be ready for data uploads, as well as how to upload data using Upload API-specific documentation

Roboflow can ingest:

You can add data to your Roboflow account by:

On the Roboflow Free Tier, all data is added via the Web UI. Start by logging into app.roboflow.com. Here you can begin adding members of your team if desired.
Invite team members to Roboflow Workspace
Select "Create New Project" . This will trigger a model to pop up with an option to upload data or go through a tutorial.
Upload your own data or download sample project tutorial
Selecting "Upload Your Own Data" requires three fields to be passed in:
  1. 1.
    Project Name
    A way to refer to your collection of images/videos.
    • If you're uploading a bunch of images of chess pieces, you might name this "Chess Data."
    • The dataset name must be unique among your datasets. (For example, you cannot have two datasets both named "Chess Data.")
    • Right now, we do not support editing the dataset name once you have created the dataset. If you must edit your dataset name, you can re-upload your data with the new name or contact us.
  2. 2.
    Project Type
    • Single-Label Classification: A good rule of thumb for when to use object detection vs classification is whether the things you're trying to predict are "objects in an image" vs "properties of an image".
    • For example, a chess piece is an "object in an image", but winter is a "property of an image". If you were trying to draw a box around the winter or daytime part of an image, you'd likely end up drawing a box around the whole thing.
    ​
    • Multi-Label Classification : Similar to Single-Label Classification in terms of finding "properties of an image", only multiple properties of an image.
      • For example, If you were trying to detect not only winter, but day, cloudy, and night as well on the same image.
    ​
    • Object Detection: Useful if you are attempting to identify one or more objects in an image with bounding boxes. A good rue of thumb is if the object will need to be detecting in motion or in position.
    • For example, a chess piece moving from one square to another, recognizing whether or not the chess pieces are where they belong on the board during the time of set up.
If you can't decide, we recommend starting out by labeling your images for object detection, because while you can convert an object detection project to a multi-label classification project easily, to convert in the other direction will require re-labeling your dataset.
  • Instance Segmentation (also known as image segmentation): Useful for when you need to measure the size of detected objects, cut them out of their background, or more accurately detect oblong rotated. With instance segmentation, your application can determine the number of objects in an image, the classifications, and their outline.
  • For example, if you need to measure the size of a tomato leaf in order to remove it from their background, or to measure a lawn from satellite imagery.
Note that instance segmentation models are typically larger, slower, and less optimized for edge deployment. Instance segmentation models may need bigger datasets to obtain the same accuracy as object detection models.
​

Roboflow currently does not offer support for other project types, however, requesting early access is highly encouraged. Below are some of the different project types that will be supported in the future and when to use them. You should only use instance segmentation if the specificity of the object's outline is required by your application.
  • Semantic Segmentation or Instance Segmentation: For attempting to identify multiple objects in images with freeform polygon shapes (not bounding boxes)
  • Semantic Segmentation: For differentiating between different objects in the same class (e.g. all cats are labeled "cat")
  • Keypoint Detection: For attempting to identify the locations of important components in an image
  1. 1.
    Annotation Group
    This should be the broader class of objects being detected or the collection of categories for a classification problem. It is a way to refer to all of the objects or labels in images.
    • For example, if when attempting to identify pawns, rooks, kings, and queen pieces on a chess board, the annotation group can be pieces.
    • Or, when attempting to classify handwritten images as being 0, 1, 2, ... 9, the annotation group can be digits.
    To learn more about annotation groups, read our blog titled What the heck is an annotation group?​

Data can also be added via the API using the API key found under settings > workspace > workspace name > Roboflow API > Generate Private API Key > Private API Key.
Once a Private API Key has been generated for a project, visit the Upload API specification to get started.

Roboflow has made available over 90,000 (and counting!) user generated datasets for you to access. From self-driving datasets to playing cards to face detection, we host diverse datasets that can be helpful to you.
If you don't need to modify the dataset in any way and you don't want to use Roboflow's one-click AutoML solution, but you'd just like to export the dataset, then click the version you want on the left-hand side underneath "Downloads" and export that version in the format of your choosing.
Want to make your dataset public? Reach out to us! We'll give you access to some of our beta features or paid features.

We strongly believe that your images and videos are yours. That's why, when you upload those, your photos and your videos remain yours -- we do not own it. (You can check out additional details in our terms of service, item 22B.)
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