YOLOv1 from Scratch

Oh boy. Hopefully this will leave you with a deep understanding of YOLO and how to implement it from scratch!

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OUTLINE:
0:00 - Introduction
0:24 - Understanding YOLO
08:25 - Architecture and Implementation
32:00 - Loss Function and Implementation
58:53 - Dataset and Implementation
1:17:50 - Training setup & evaluation
1:40:58 - Thoughts and ending

[AladdinPersson]

Here's the outline for the video:
0:00 - Introduction
0:24 - Understanding YOLO
08:25 - Architecture and Implementation
32:00 - Loss Function and Implementation
58:53 - Dataset and Implementation
1:17:50 - Training setup & evaluation
1:40:58 - Thoughts and ending

[MohamedAli-dkcb]

One of the greatest deep learning videos I have ever seen online. You are amazing Aladdin, please keep going with the same style. The connections you make between the theory and the implementation is beyond PhD level. Wish I can give you more than one like.

[LinshuaiDuan]

I've been looking for a YOLO revival for two days, and you were the most detailed. With the highest respect

[asiskumarroy]

I dont know how do I express my gratitude to you.Thanks a lot brother.

[vijayabhaskar-j]

This series was super helpful, can you please continue this by making one for Yolo v3, v4, SSD, and RetinaNet? That will make this content more unique because none of the channels that explains all these architectures and your explanations are great!

[nguyenthehoang]

By far, your series is one of the best content about computer vision on YouTube. It's very helpful when people explain how things work under the hood, like the very well-known courses by Andrew Ng. If you make a paid course for this kind of content, I'll definitely buy it.

[_nttai]

I was lost somewhere in the loss but still watch the whole thing. Great video. Thank you

[Anonymous-nzwd]

GOD DAMN! I was searching for this for a really long time but you did it, bro. Fantastic.

[rampanda]

The savior, Been looking at codes of other people for few days, Could not understand it better as those were codes only with no explanation what so ever. Thank you very much.

[haldiramsharma]

Best channel ever!! All because of you, I learned to implement everything from scatch!! Thank you very much <3..

[_adi_]

This channels going to blow up now. Great stuff!

[caidexiao]

Thanks a lot for you kindness to provide the yolov1 video. By the end of the video, you got mAP close to 1.0 with only 8 training images. I guess you used weights of a well trained model. With more than 10, 000 images and more than 20 hours on Kaggle 's free GPU, my mAP was about 0.7, but my validation mAP was less than 0.2. Nobody mentioned the over fitting issue of yolo v1 model training.

[sangrammishra]

I love the way he explained and always maimtain simplicity in explaining the code, thanks aladdin

[thanhquocbaonguyen]

massively thank you for implementing this in pytorch and explain every bits in detail. it was really helpful for my university project. i have watched your tutorials at least 3 times. thank you!

[keshavaggarwal]

Best Channel ever. Cleared all doubts about YOLO. I was able to implement this in tensorflow by following your guide with ease. Thanks a lot bro.

[Тима-щю]

What an amount of work! I don't often see people in the internet that are so dedicated to deep learning!

[eminemhc]

Only 3.5K subscribers ??? One of the most underrated channel in YouTube
Keep posting quality video like this bro, soon you will reach 100K+ subs, congrats in advance
Thanks for the quality content :)

[crazynandu]

Great Video as usual . Looking forward to see RCNNs (mask, faster, fast, ..) from scratch from you !! Similar to Transformers you did, you can do one from scratch and other using the torchvision's implementation .Kudos !!

[张子诚-zb]

I'm a beginner of object detection, You videos help me a lot. I really like your style of code.

[sachavanweeren]

I can imagine this video took a lot of time to prepare, the result is great and super helpful. Thank you very much. Respect!