Roboflow is a framework used in Computer Vision for better data collection to preprocessing, and model training techniques. Roboflow has public datasets readily available to users and has access for users to upload their own custom data also. Roboflow accepts various annotation formats. In data pre-processing, there are steps involved such as image orientations, resizing, contrasting, and data augmentations.

The entire workflow can be co-ordinated with teams within the framework. For model training, there’s a bunch of model libraries already present such as EfficientNet, MobileNet, Yolo, TensorFlow, PyTorch, etc. Thereafter model deployment and visualization options are also available hence encompassing the entire state-of-art.

Roboflow is used in various computer vision industries for use cases such as – gas leak detection, plant vs weed detection, aeroplane maintenance, roof damage estimator, satellite imagery, self-driving cars, traffic counter, garbage cleaning, and many more.

Steps To Use Roboflow in Object Detection:

1.Dataset Loading 2.Labeling 3.Organise 4.Process 5.Train 6.Deploy 7.Display Roboflow has an account set up for each user. Now we will discuss each of the steps in the task of object detection.m.

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