PaddlePaddle Deep Dive: Speed, Efficiency, & Its Edge in Object Detection & Segmentation
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In this computer vision tutorial, we will introduce you to PaddlePaddle framework and we'll also show you how object detection, pose estimation and segmentation works on PaddlePaddle using a Google Colab notebook.
Our spotlight is PaddlePaddle in which new benchmarks have positioned PaddlePaddle as an emerging leader, touting speeds that surpass many industry mainstays. In this video, we delve into the intricacies that make PaddlePaddle efficient, its real-world applications in computer vision tasks like object detection, pose estimation, and segmentation, and how it measures up against other well-established frameworks. Discover why adding PaddlePaddle to your toolkit might just give you a competitive edge in the AI landscape.
Topics Covered
✅How PaddlePaddle Achieves Superior Speed
✅PaddlePaddle for Object Detection
✅PaddlePaddle for Keypoint Detection
✅PaddlePaddle for Image Segmentation
⭐️ Time Stamps:⭐️
00:00-00:14: Introduction
00:14-01:13: PaddlePaddle
01:13-01:25: Features of PaddlePaddle
01:25-01:42: Data Parallelism
01:42-02:03: Model Parallelism
02:03-02:23: TensorRT
02:23-02:41: Model Support
02:41-05:36: Paddle Detection
05:36-07:58: Paddle Seg
07:58-08:17: Outro
Resources:
🖥️ On our blog - learnopencv.com/ we also share tutorials and code on topics like Image
Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.
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