Let's move to Media Tools.
These tools start with files I already have.
The Image Toolkit lets me process one image or a batch using the same selected settings.
I can crop, resize, compress, convert the format, rotate, or flip the images.
The resize choices include fitting inside a size, filling and cropping, fitting with padding, or using exact dimensions.
There is also an option to allow upscaling.
For appearance changes, I can adjust brightness, contrast, saturation, sharpness, blur, grayscale, and auto contrast.
I can add text, choose its size and color, and add a prefix or suffix to the batch filenames.
In this version, added text uses a built-in font and appears at the top left.
The output choices include WebP, JPEG, PNG, GIF, BMP, and TIFF.
If I convert a transparent image to JPEG, it needs a background color because the JPEG result doesn't retain alpha transparency.
The interface lets me choose that background color.
The image page shows the original preview and the processed result.
I can also choose to remove metadata, including location information.
Then I process the files and download the results from Media Jobs.
The Video Toolkit gives me a similar workflow for video.
I upload a file and choose the action I want.
I can trim a time range, crop the frame, resize, compress, or convert to MP4, WebM, MOV, AVI, or MKV.
For audio, I can keep it, remove it, or extract it as an MP3 file.
I can also make a GIF or extract frames to a ZIP.
The adjustment controls include rotation, horizontal or vertical flipping, speed changes, grayscale, frame rate, and metadata removal.
The processing summary helps me see which changes I've selected before running the job.
Video compression has a quality tradeoff.
A smaller file can lose detail, and the final size can differ from an estimate.
The job list shows progress and downloads.
These media jobs are kept separate from annotation projects and professional service orders.
The current allowance is three free guest jobs and seven free jobs for a free signed-in account.
Paid plans remove that job-count quota.
Upload, output, duration, and other processing limits still apply.
The page shows the current limits so users don't have to guess from a plan name.
There are also eight focused dataset and vision utilities.
Each has a guide explaining the expected input, the output, and the limitations.
The first is Dataset Health Check.
This looks at the file structure, whether images can be read, their dimensions, and whether image and label names match.
It can flag an image with no label or a label with no corresponding image.
It produces a report without changing the original ZIP.
The second is Annotation Format Converter.
It takes a YOLO bounding-box dataset and converts it to COCO JSON or Pascal VOC.
The current converter expects the five-value box format.
It doesn't convert YOLO segmentation polygons or every possible annotation format in both directions.
The third is Video Frame Extractor.
I choose a time range, an interval in seconds or frames, an output image format, and a maximum count.
The tool gives me a ZIP of extracted images.
For example, a twenty-second range sampled every two seconds gives about ten frames.
That's twenty divided by two.
This is a planning estimate. The decoded output is what I actually inspect.
The fourth is Dataset Splitter.
It makes training, validation, and test folders with matching labels.
Its defaults are seventy, twenty, and ten percent.
It has a shuffle seed so the same input can be split repeatably.
With one hundred images, those percentages request seventy training images, twenty validation images, and ten test images.
The splitter works at the file level.
It doesn't group by video or recording session and doesn't stratify by class.
If my data comes from several videos, I would organize the split by recording when I need to test performance on unseen videos.
Randomly separating neighboring frames can give an overly easy test.
The fifth utility is Duplicate Image Finder.
It reports exact duplicates and images with matching compact visual hashes.
It doesn't delete anything.
I inspect a visually similar group before deciding what to remove.
The visual hash can group different pictures or miss some near-duplicates.
The sixth is Annotation Quality Checker.
This checks supported YOLO box rows for malformed lines, invalid value ranges, and empty or missing labels.
It helps with formatting.
It doesn't know whether a box encloses the intended object.
It also isn't a complete geometric or segmentation-label audit.
The seventh is Auto-Anonymize.
It detects frontal faces in a still image and blurs the detected regions.
I can choose the blur strength.
Then I inspect the output because the detector can miss a profile, a small face, or a blocked face.
It doesn't automatically blur license plates, identifying text, or an entire video.
The eighth is Background Remover.
It estimates the main foreground and exports a transparent PNG.
I would inspect the edges over light and dark backgrounds, especially around hair or low-contrast boundaries.
The foreground estimate can help prepare an image.
It isn't automatically a reviewed segmentation label.
The dataset guides also offer a small example ZIP.
It contains simple labeled rectangles and a duplicate example.
That lets someone learn the file workflow before uploading their own dataset.
Now let's look at the professional annotation service.
This is for someone who wants annotation work done as a service.
It's a separate one-time order.
The user enters project information, requested classes, annotation instructions, approximate image count, preferred export format, and requested completion date.
They can provide a private cloud link or upload a dataset ZIP directly.
There are also places for a sample image and an annotation example.
Submitting the quote request doesn't collect payment.
The service first inspects a representative sample, around ten to twenty images, to understand the actual scope and prepare the quote.
The listed starting prices are six cents per bounding-box object and eighteen cents per standard polygon object.
Standard polygons are described as having up to thirty points.
The minimum order is fifty dollars before the first-order launch discount.
The listed launch discount is twenty percent, capped at fifty dollars.
The work is priced by object.
Two images with very different numbers of objects can require different amounts of work.
For example, one thousand box objects at six cents each gives a sixty-dollar starting subtotal.
That's one thousand times zero point zero six.
A final quote can change with object visibility, polygon complexity, class count, image quality, and delivery requirements.
The user reviews and approves the quote, then pays through the service payment workflow.
Work starts after payment is confirmed.
The order page shows progress using completed and total image counts.
The process includes a quality-review stage before delivery.
The user receives a private ZIP and has a seven-day review period.
One reasonable correction round is included.
Delivered files are described as available for thirty days before retention cleanup removes them.
The service accepts clearly identifiable physical objects.
Its policy excludes medical images and subjective atmospheric regions such as smoke, fog, rain, clouds, and shadows.
So a request involving those regions falls outside the current service scope.
On the owner side, there are controls for reviewing requests, preparing quotes, updating order progress, handling corrections, and delivering files.
That keeps the service order organized separately from a self-service AnnoTrack project.