Hasty is an image annotation tool for creating ground-truth datasets to be used in machine learning applications. Unlike many similar tools, Hasty uses machine learning in the tool itself. This allows you to annotate exponentially quicker than before.
To get started with Hasty, go to https://app.hasty.ai/ and sign up either by using your email or by logging in through your Google account.
If you want to know more before signing up, you can go to https://hasty.ai.
To log in or sign up with your Google account, just click the "Login with Google" button on the first screen. After clicking the button, a new window will open where you can fill in your Google account and password. Do so and you will be logged in (if you have used Hasty before) or signed up (if you haven't used Hasty before).
To signup/login with an email address click the "Login with email" button. On the next screen, you will be asked for an email, fill in the email address you want to use and then click on "Continue". When you do so, an email containing a link will be sent to the email you specified. Open up the email, click the link, and then you will be logged in (if you have used Hasty before) or signed up (if you haven't used Hasty before).
After you have logged in, you will find yourself in the project overview screen.
From here, you can either create a new project, change existing projects, or start testing out our already existing demo project.
We already mentioned that Hasty uses machine learning to speed up the process of annotation. However, our AI tools are not available when you are starting a new project. For these tools to work, we first need you to create enough annotations manually so that we can generate a model.
To activate the tools, you will need to completely annotate either ten images (and set their status to "done" or "to review"), or in the case of the Class Predictor Assistant, create 25 annotations.
After giving the model(s) some time to train, you will be able to start using our AI tools. Of course, suggestions might not be perfect from the beginning but the models will improve as you annotate more images. In our experience, annotating around 10 annotations per label class is generally a good rule-of-thumb for achieving useful results.
Can’t find what you are looking for in our documentation? Get in contact with us through one of the channels below to get help:
email us at firstname.lastname@example.org