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Saved by @ankitsrihbti

I’m at Day 23 of my 30 posts (on Object Detection) in 30 days challenge

I gathered 12 visual summaries on OD Modeling 🎁

A lot of people find those posts helpful, follow @ai_fast_track to catch the upcoming posts, and give this tweet a quick retweet 🙏

Summary of summaries👇

 1- Common Object Detector Architecture you should be familiar with:

https://t.co/qrJ2ii5npT
2- Four Feature Pyramid Network (FPN) Designs you should know:

https://t.co/gqdf45R81q
3- Seven things you should know about the Focal Loss

https://t.co/3vrM7TSmts
4- FCOS is the first anchor-free object detector that beat two-stage detectors

https://t.co/Qakx6JjQQw
5- YOLOX beat YOLOv5!

https://t.co/UNEHuAfl3E
6- How easy creating YOLOV5 and YOLOX models in IceVision

https://t.co/2Io3NA8147
7- VFNet: A very interesting model that isn’t under the radar

https://t.co/4nH5t9nuam
8- YOLO Real-Time (YOLO-ReT) architecture targets edge devices.

It achieves 68.75 mAP on Pascal VOC and 34.91 mAP on COCO

https://t.co/svk3BeSbek
9- Similarities and the differences between some popular Object Detection models.

https://t.co/s9Jt8eLYIY
10- FCOS3D won the 1st place out of all the vision-only methods in the nuScenes 3D Detection Challenge of NeurIPS 2020.

https://t.co/JmY30x57S2
11- The Generalized Intersection over Union (GIoU) can be used as a metric as well as a loss function

https://t.co/UZIcpPJN9N
12- What is the Average Precision (AP), mean AP (mAP), and COCO Metric?

https://t.co/HceU2w44v4
⭐️ If you find those summaries helpful, feel free to follow @ai_fast_track for more OD / CV demystified content in your feed

⭐️ If you could give the thread a quick retweet, it would help others discover this content. Thanks!

https://t.co/3EVP8yBDk8

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