Roboflow - Best way to build end-to-end computer vision pipeline

We recently built a logo detection system from a single volleyball match video. The entire pipeline was powered by Roboflow with YOLO as the model backbone. What stood out was how much time we saved by keeping everything inside one platform.

The process looked like this:

Upload the volleyball match video. Roboflow automatically extracted frames.

Define classes for each sponsor logo and annotate collaboratively with teammates. The review tools made it easy to keep labeling consistent.

Roboflow created dataset versions and automatically split them into train, validation, and test sets.

We could choose between training from scratch, fine-tuning pretrained YOLO weights, or even starting with public datasets and models from Roboflow Universe.

During training, Roboflow displayed dynamic graphs of precision, recall, and mAP per epoch, which made it easy to track progress.

We tested the model on a new video right inside the browser and on a mobile phone.

Finally, we deployed the model instantly with a shareable link or QR code.

What I like about Roboflow is that it truly supports an end-to-end workflow:

Frame extraction from video

Collaborative annotation and review

Dataset versioning and augmentations

Access to Roboflow Universe (public datasets and models)

One-click training with real-time metrics

Easy testing on new images or videos

Instant deployment with link or QR code

For us, this meant moving from raw video to a working, shareable model in days instead of weeks. It is a clean way to build computer vision pipelines when you want speed, collaboration, and reproducibility.