Global plastic production has exceeded 500 million tons. Moreover, estimates
from the US Environmental Protection Agency
So you're working on building a machine learning model, and you have hit the realization that you will need to annotate a lot of data to build a performant model. In the machine learning meta today, you will be bombarded with services offering to fully outsource your labeling woes.
When we are teaching a machine learning model to recognize items of interest, we often take a laser focus towards gathering a dataset that is representative of the task we want our algorithm to master.
The YOLO family recently got a new champion - YOLOR: You Only Learn One Representation. In this post, we will walk through how you can train YOLOR to recognize object detection data for your custom use case.
Creating a computer vision model, at the outset, seems like a pretty involved
task. Even if you’re using an end-to-end solution
[https://blog.roboflow.com/what-does-end-to-end-really-mean/] like Roboflow, the
Roboflow is a tool for building robust machine learning operations pipelines for
computer vision: from collecting and organizing images, annotating, training,
deploying, and creating active learning [https://blog.roboflow.com/
Developing, deploying and optimizing computer vision models used to be a
cumbersome, painful process. With Roboflow, we sought to democratize this
technology, which (first and foremost) meant knocking down the
Featuring rock, paper, scissors.
OpenAI's CLIP model [https://models.roboflow.com/classification/clip]
(Contrastive Language-Image Pre-Training) is a powerful zero-shot classifier
that leverages knowledge of the English language
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
Using transfer learning
[https://blog.roboflow.com/a-primer-on-transfer-learning/] to initialize your
computer vision model from pre-trained weights rather than starting from scratch
(initializing randomly) has been shown to increase performance
IBM recently announced they are shutting down IBM Visual Inspection, their
product for creating custom computer vision models for classification and object
detection. No new instances can be created and