Okay, let me walk you through my website, Tools in Vision, and explain what each part does.
The part I want to spend the most time on is AnnoTrack. That's where we label images, work with video frames, track objects, review annotations, and prepare datasets for training.
Around AnnoTrack, there are several other tools. AI Studio creates images, videos, synthetic datasets, and narration. Media Tools processes existing images and videos and checks datasets. The professional annotation service lets someone request labeling work. And the Learning Center explains the ideas behind these tools.
I'll use one example throughout the walkthrough. Let's say we're working on a project that needs to identify cars in a parking lot.
We start with a video. We extract useful frames from it, label the cars, check the labels, and export the dataset. If several people are working together, we also need a way to review their work and see what's finished.
That example gives us a reason for each part of the website. The media tools prepare the files. AnnoTrack creates the labels. The team features organize the review. And the Learning Center explains concepts like bounding boxes, segmentation, and training data.
The prices and limits I'll mention belong to this version of the website. The account and tool pages show the settings available to the user.
Let's start with the home page.
The main navigation takes us to AI Studio, AnnoTrack, Annotation Service, Media Tools, and Learning Center.
These areas use the same Tools in Vision account. The user doesn't need a separate account for every tool.
The account page gives a quick overview of the profile, AI credit balance, and AnnoTrack subscription.
The subscription and the AI credit balance serve different purposes.
The AnnoTrack subscription controls things like storage, export access, project AI training, and team features. AI credits are a prepaid balance used for generation.
So buying an annotation subscription doesn't mean every AI generation is included. The account page shows those separately so the user can understand what they have.
The sign-in page supports email and password. It also has Google and GitHub sign-in options when those providers are configured.
There is email verification, password recovery, and an account security page where the user can check recognized devices and login history.
Once I'm signed in, I can open AnnoTrack and choose a project.
A project keeps a dataset organized. I could have one project for parking-lot cars, another for pedestrians, and another for an industrial inspection task.
The Personal Workspace contains my own projects. The Team Workspace contains projects shared with the team.
Each workspace has its own access and project list. Being a viewer in a team doesn't remove the user's ability to work in their own personal workspace under their personal plan.
On the project page, I can create a project, open it, rename it, or delete it.
There are also copy and move controls between the personal and team workspaces, when the user's permissions allow that.
Copy keeps the project in the original workspace and creates a copy in the destination. Move transfers the project to the other workspace.
Once I open a project, the image appears on the left. This is the canvas where I draw and edit annotations.
The controls are on the right. They show which account, workspace, and project I'm using. They also show storage information, the image name, the annotation mode, and the available tools.
The project information becomes especially useful when we get to AI tracking.
Each project has its own training examples and its own trained model. A model trained for one project doesn't automatically become the model for every other project.
For our parking-lot example, the next step is adding images.
If the images are already prepared, I can select several files and upload them together.
The page shows upload progress and the limits for the account. The actual limits depend on the plan and the available storage.
If I have a video, I can extract its frames directly into the project.
I choose the video, select the part I want, and decide how often to extract a frame.
There are two ways to describe the interval.
I can use time, such as one image every second. Or I can use a frame interval, such as every thirty frames.
Those mean the same thing only when the source has the appropriate constant frame rate. With a variable frame rate, the difference matters.
I can choose JPEG, PNG, or WebP for the extracted images and set a maximum number of frames.
The page gives an estimate before extraction. After extraction, each frame becomes a normal image in the annotation project.
For tracking, I want those images in the correct sequence.
If I jump from one unrelated picture to another, there isn't a sensible movement for the tracker to follow.
If I sample too far apart, the object might move a long distance between images, which can make tracking more difficult.
There's also a separate frame extractor in Media Tools.
The difference is where the frames go.
The video import inside AnnoTrack puts the frames directly into the project so I can annotate them immediately.
The separate extractor gives me a ZIP file of frames that I can download and use elsewhere.
Once the images are in the project, I can use Previous and Next or choose a particular image from the image list.
I don't have to open every image in order when I'm checking a specific frame.
Now let's talk about bounding boxes.
A bounding box is a rectangle around an object. It tells a detection model where the object is and which class it belongs to.
For this example, I select the car class and draw a rectangle around a car.
I can select that box, move it, and adjust its edges until it fits the object properly.
There's also an Add New Box In Center button. That creates a starting box that I can move and resize.
The class is the name of the thing we're labeling. It could be car, truck, person, or another category defined for the project.
The class controls let me add a class, rename a selected class, delete a class, or assign the selected class to an existing box.
Renaming a class is handled across the saved project annotations so its name stays consistent.
The box and the class describe two different things.
The coordinates describe the location. The class describes what is at that location.
A perfectly placed box with the wrong class is still a bad label.
If I make a mistake, I can delete the selected box.
There is also a button to delete all boxes on the current image. That is separate from deleting the image itself.
The image deletion controls are in the Images section.
For a large image, I can zoom in with the mouse wheel.
I can pan by holding the Space key while dragging.
Fit Image and Reset View bring me back to the full view.
The minimap helps me see which part of the image I'm looking at while zoomed in.
There are keyboard shortcuts too.
The A key or the left arrow goes to the previous image. The D key or the right arrow goes to the next image.
Delete removes the selected annotation.
Control and Z handle undo. Control and Y handle redo.
The workspace has autosave and a save-status indicator.
I still watch that indicator and use the explicit Save button when I want to confirm that the work is saved.
Tracking suggestions have an extra review step. I'll explain that after we look at segmentation.