Roboflow | Use Stable Diffusion to Generate Synthetic Data to Train Computer Vision Models @Roboflow | Uploaded 1 year ago | Updated 6 hours ago
Written Guide - blog.roboflow.com/synthetic-data-with-stable-diffusion-a-guide
Roboflow - roboflow.com
Roboflow Universe: universe.roboflow.com
Github - github.com/roboflow-ai/stable-diffusion-demo
Written Guide - blog.roboflow.com/synthetic-data-with-stable-diffusion-a-guide
Roboflow - roboflow.com
Roboflow Universe: universe.roboflow.com
Github - github.com/roboflow-ai/stable-diffusion-demo
![[LIVE CODING] Visualizing Football Plays with Computer Vision (Part 1)](https://i.ytimg.com/vi/QI1dQF1iw0c/hqdefault.jpg "[LIVE CODING] Visualizing Football Plays with Computer Vision (Part 1)
In this live session well use the Football Player Detection dataset[1] by Scott Bronkema[2] shared on Roboflow Universe to create a program that tracks players movements using zero-shot object detection[3] in a football play and visualizes the result.
Tools well use/cover include: Python, Roboflow, YOLOv4, YOLOv5, CLIP, zero shot object tracking, ffmpeg, and OpenCV.
Update: we sent an improved dataset to training at the end of the video; well pick up here once the improved model is finished training with part 2. Subscribe and ding the bell to be notified!
[1] https://universe.roboflow.com/bronkscottema/football-player-detection
[2] https://www.linkedin.com/in/scott-bronkema-09389824/
[3] https://blog.roboflow.com/zero-shot-object-tracking/")
