Mean Average Precision (mAP) Explained and PyTorch Implementation
In this video we learn about a very important object detection metric in Mean Average Precision (mAP) that is used to evaluate object detection models. In the first part of the video we try to understand how this method works and then move on to PyTorch to implement this from scratch. ❤️ Support the channel ❤️ / @aladdinpersson Paid Courses I recommend for learning (affiliate links, no extra cost for you): ⭐ Machine Learning Specialization https://bit.ly/3hjTBBt ⭐ Deep Learning Specialization https://bit.ly/3YcUkoI 📘 MLOps Specialization http://bit.ly/3wibaWy 📘 GAN Specialization https://bit.ly/3FmnZDl 📘 NLP Specialization http://bit.ly/3GXoQuP ✨ Free Resources that are great: NLP: https://web.stanford.edu/class/cs224n/ CV: http://cs231n.stanford.edu/ Deployment: https://fullstackdeeplearning.com/ FastAI: https://www.fast.ai/ 💻 My Deep Learning Setup and Recording Setup: https://www.amazon.com/shop/aladdinpe... GitHub Repository: https://github.com/aladdinpersson/Mac... ✅ One-Time Donations: Paypal: https://bit.ly/3buoRYH ▶️ You Can Connect with me on: Twitter - / aladdinpersson LinkedIn - / aladdin-persson-a95384153 Github - https://github.com/aladdinpersson 0:00 - Introduction 0:09 - Explanation of mAP 8:19 - Implementation in PyTorch
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