Image Classification model VGG16 from scratch | Computer Vision with Keras p.7

You can Build Computer Vision software to DETECT and TRACK any Object.
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We will see how to make the VGG16 model from scratch with Keras, I will enter all the steps until we arrive at the result with Keras.
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#keras #computervision #VGG16

[@ghaithalmasri3039]

I can’t believe you’re doing this for free. You’re a legend

[@bhushandeshpande7007]

I really love the playlist and I coded everything with you. I worked and tried to understand the VGG16 before, but no one explained this as easily and clearly as you. Thank you for your teaching. I am undergrad from Indian Institute of Science Education and Research (IISER) from India. I would like to communicate with you. Thank you once again.

[@leo1722467]

The easiest way to understand for me so far, it's your vídeos. Ty for your effort.

[@arduinouno1285]

Thanks a lot, this help me understand the concept without having to go too deep into the theory

[@cvicracer]

Thanks for your videos,they really are well done. I always learn alot from them.

[@pysource-com]

glad to hear that ;)

[@loganwoolfson5823]

Hi, I have tif images which are have 5 channels instead of the usual 3. How can I adapt the code to run these multispectral images? Any help would be greatly appreciated.@@pysource-com

[@inhibited44]

Took coursera class in Machine Learning, but getting much more detail and more comfortable with this. Trying to model 200 pictures of 5 objects and not getting a match yet.

[@HariRavi]

Great content. Keep publishing these contents. You explained everything clearly.

[@oliwiaroczniak4686]

Thank you very much please continue with this series

[@Set1ish]

Good job! My suggestion is to show how to train the model by using different picture cats, dogs, hedgehog to train the model and then test it on a new picture of dog and see the response of the model

[@ramikawamleh]

many thanks for sharing, you have greate way for delivering the idea. hope to provide the training phase and focus on how to detect and avoid overfitting during training.

[@pysource-com]

slowly we will cover different aspects like training, image preprocessing, augmentation and so on.

[@user-wm1ob3vs2t]

Do you have a video(paid for or free) that shows how to train a model for specific categories and train it and then deploy it in python?

[@redabakkouch3764]

thanks man. love this playlist. hope to see more

[@maheshikaharischandra490]

Excellent explanation, thank you.

[@justinamichael316]

Extremely helpful...

[@rezat3]

Very helpful. Thanks a lot.

[@nikostap]

Thanks for your video. Its soi interesting.

[@VincentGonzales-zg6xg]

Thank you this was very helpful!

[@amrzakaria5290]

Good job , thank a lot.

[@AxelWeber-gx2yt]

Thank you

[@paul71249]

thank you bro

[@shankar.parmargec5976]

very good video.. but I dont know hpw to use this model for classification.?

[@annyd3406]

that was very helpful thank u very much

[@ahmedhamdy-fz1lb]

Thanks a lot, great video.

[@luukschalkwijk8043]

Can you also do identification with vgg16

[@srijanairon4515]

when will the next video of th series be out it helped quite alot....

[@eleojoadegbe]

Thank you so much

[@vvnchao]

Thanks for the video 🎉

[@daljeetsinghranawat6359]

i am working on mask rcnn in my post graduate degree.....i was wondering if i could impliment Faster RCNN from scratch....i know it uses RESNET which uses skip connection...........this video was very helpful in understanding out features are made...and one question that how output of last conv pooling layer...which has 7x7 images is mapped to first dense layer....i gues thats just (7x7x512)--->4096

[@goldiesmummy5008]

very nice work bro

[@cltee6629]

thanks good video. can you pls do anomaly detection? tq

[@mersibonmelese1262]

Great

[@cluelessarn]

Thx

[@antonpivnenko6711]

good video

[@swatyk6881]

Thanks for this video…..

[@lioramar6420]

thanks for the video, can you teach us how to train a model that works fast on raspberry pi like the model.tflite?

[@marklawley3315]

Great video. How do you tell it that 1 is hedgehog, 2 is cat, and 3 is dog?

[@leamon9024]

Thanks man! Love this series. Hope there's a video about how to tune a CNN model performance or tackle issue when data is imbalanced.

[@ishaat_plays]

Awesome Demonstration Sir. Could you make a video of Recurrent Neural Network for Computer vision in Real time Natural Language Processing ?

[@pysource-com]

I might do that in the future, but not soon as I'd like to show and explore more in depth Object Detection and Segmentation models

[@GonnyHuy]

can you taking about pthon opencv connect plc mitsubishi?

[@raj4624]

Brother your videos are so useful. Your instructions and teaching also top class . BTW your website not working while purchasing courses i tried multiple time but failed

[@pysource-com]

Hi Raj, thank you for your feedback.
please contact me here [e-mail] and we'll assist you with that

[@ahamednadeemm5112]

how to do multiclass image classification ?

[@neilyoung6671]

37:xx - I'm not getting the step from Dense(3) to hedgehog, cat, dog or Dense(2) to cat, dog. How is the number of units related to the category? Why is a Dense(3) not also car, trafficsign and people? Would you mind to explain?

[@neilyoung6671]

Also: You get 0.39 for hedgehog, 0.18 for cat, 0.419 for dog, so basically the hedgehog was classified as dog?

[@KaranChecker]

How's 224 equal to 20-24? Baah

[@pysource-com]

where?

[@hadarpinhas123]

@@pysource-com The automatic translation accidently generates subtitles of "2024" every time you say 224, e.g. at 13:51

[@pysource-com]

@@hadarpinhas123 oh ok, thanks for pointing that out

[@cvicracer]

Thank you very much please continue with this series

[@chebiliameur1397]

Excellent explanation, thank you.

[@ratfuk9340]

Thank you

[@user-cz5tk1bd9q]

thank you