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.
Computer vision problems start with finding high quality image datasets.
Fortunately, access to common image data is increasingly easier. Datasets like
Microsoft's COCO dataset [https://blog.roboflow.com/coco-dataset/] and
The computer vision research community benchmarks new models and enhancements to
existing models to test model performance. Benchmarking happens using standard
datasets which can be used across models. With this
At Roboflow, we often get asked, what is the train, validation, test split and why do I need it? The motivation is quite simple: you should separate you data into train, validation, and test splits to prevent your model from overfitting and to accurately evaluate your model.
Computer vision is performed on a wide array of imaging data: photographs,
videos [https://blog.roboflow.com/using-video-computer-vision/]. Commonly, this
data is captured
In a given year, approximately 65,000 workers wearing hard hats
incur head injuries in the workplace, of which over one thousand
As global coronavirus case numbers continue to climb, troubling stories of
hospital shortages, deaths, and disrupted communities fill the news. Frankly, it
can leave one feeling disempowered – especially when the
And that's a problem that is extremely dangerous.
Machine learning, the process of teaching computer algorithms to perform new
tasks by example, is poised to transform industries from agriculture