Recent breakthroughs in large language models (LLMs) and foundation computer vision models have unlocked new interfaces and methods for editing images or videos. You may have heard of inpainting,
This is a guest post by Kristen Kehrer
, Developer Advocate at CometML [https://www.comet.com/site/]. Since
NVIDIA's TAO Toolkit provides a framework for fine-tuning popular computer vision models using your own data. In this tutorial, we'll be demonstrating how to use Roboflow to curate a high-quality computer vision dataset to use with NVIDIA's TAO Toolkit.
Benefits to Existing Models
Polygons have traditionally been used for training image segmentation models
polygons can also improve the training of object detection models
Applying data augmentations
one of the most essential steps when developing your dataset. Roboflow offers a
wide variety of augmentations that you can apply
When creating computer vision models, data augmentation
[https://docs.roboflow.com/image-transformations/image-augmentation] can improve
model performance with an existing image dataset. Image augmentation increases
the size and variability of
Ok, so you've trained a model
[https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/] and it's not doing as
well as you'd hoped. Now what? You could experiment with augmentation
Computer vision data augmentation
a powerful way to improve the performance of our computer vision models without
needing to collect additional data. We create
The "secret" to YOLOv4 isn't architecture: it's in data preparation.
The object detection space [https://blog.roboflow.com/object-detection/]
continues to move quickly. No more than two months ago, the
The appeals of synthetic data are alluring: you can rapidly generate a vast amount of diverse, perfectly labeled images for very little cost and without ever leaving the comfort of your office. The good news is: it's easy to try! And we're about to show you how.
Flipping an image (and its annotations) is a deceivingly simple technique that
can improve model performance in substantial ways.
Our models [https://models.roboflow.ai] are learning what collection of