How to Train YOLO Object Detection Models in Google Colab (YOLO11, YOLOv8, YOLOv5)

[@oludelehalleluyah6723]

Yooo... It as been so long... I have learnt so much from you... I learnt how to build object detection models with tensorflow and it has gone a long way in helping me... Good to have you back

[@Jdmr1701]

Thank you for posting this. It’s very apropos for me at the moment. I am trying to train a model to identify our dogs for a larger project. I am new at the AI stuff and not a coder, but this is the kind of information I have been searching for the last several days. Thank you!

[@PixlPainter_]

Thanks a lot for your effort! This video is exactly what I needed, great job explaining! Can't wait for the article on deploying on a Raspberry Pi 🎉

[@EdjeElectronics]

Thanks for the kind words, and I'm glad you found it useful! I'm shooting to have the Pi video out before the end of January. Cheers!

[@fernando2074]

Thank you so much!!! Not only great tutorial but also inspirational spirit, so grateful for this😊

[@paulokaedolima2739]

You helped so much in my Cardgame Tracker! Thanks!

[@mickfone4821]

Thank you very much, it's so cool.

[@shashankvray9042]

Good video. Thanks for the comprehensive explanation. Can you start a whole new playlist on GenAI and LLM models with more in-depth explanation that includes explanations on fine tuning methods like PEFT as well

[@mr.rizcarn4633]

Amazing tutorial

[@EdjeElectronics]

Weird that it's detecting other objects! The model will only detect classes listed in the data.yaml file (created at 11:53 in the video). Double-check your classes.txt and the data.yaml file to confirm there aren't extra classes listed.

[@mr.rizcarn4633]

Hi sir it's all good now, thank you. But my new problem is, it detects other objects as one of my classes. For example: It detects the class name "bottle" even though I present a TIN CAN (which is not on my class list)

[@creativearts4917]

you r the person i was looking for from almost 2 months... i have watched almost all this type vids... have you did any comparison between yolo/ultralytics and tensorflow?

[@EdjeElectronics]

Ultralytics YOLO models are way better than TensorFlow detection models. They're faster, more accurate, more modern, and easier to train. I get 7 FPS with a 640x640 YOLO11n model on my Raspberry Pi 5 vs 4 FPS with a TensorFlow SSD-Mobilenet model. Here are a couple links to look at: TFLite comparison: www.ejtech.io/learn/tflite-object-detection-model-comparison YOLO comparison: kzbin.info/www/bejne/lYiuhGd7brigoaM

[@kessbeats603]

good to see you. keep it up

[@BadHits-c5i]

What if I don't have a gpu then which pytorch to install

[@EdjeElectronics]

Then you can just use "pip install ultralytics" (which we do at 17:20 in the video). This installs the CPU version of PyTorch by default.

[@milankarakas3005]

After detecting, can you use python script to make an action? I have idea to "ring the bell" when one of my cats want to enter. There is a camera watching the door. Until now, I should to watch all the time, but what if Yolo can detect my cat and notify me? If not my cat, ignore.

[@EdjeElectronics]

@@milankarakas3005 haha I made a video about that exact project! Check out my Raspberry Pi Pet Detector. It’s very dated now and the code will look different with YOLO. But yes it is certainly capable of that! kzbin.info/www/bejne/nXjUh4GrjtSfbNE

[@pludwig2820]

Great videos and well documented tutorials! I would like to ask if there is any way to automatically transform your PascalVoc formatted custom images ( made for Tensorflow ) into Yolo related ones ?

[@EdjeElectronics]

Thank you! Yes, here's one way to convert Pascal VOC formatted data to YOLO format. These commands will download a conversion script and then run it. The arguments for the script are 1) the path to the labels directory, 2) the path to the classes.txt labelmap file, and 3) a name for an output file. When you run the script, it will create a .txt file with data in YOLO format from every .xml file in the labels directory. curl -o convert_voc_yolo.py raw.githubusercontent.com/geaxgx/playing-card-detection/refs/heads/master/convert_voc_yolo.py python convert_voc_yolo.py path/to/labels/dir classes.txt converted.txt Good luck :| if that's complicated, Roboflow can convert data formats too.

[@krupaltisgaonkar238]

Great Video! I really need this for my ML project. However, I already have created a dataset using LabelImg and created xml files for a Tensorflow format. Do you know how I can change it to a YOLO format? NEW SUBSCRIBER!!!!!!!!!

[@EdjeElectronics]

Thanks, glad it's helpful! Yes, here's one way to convert TensorFlow data (which is in Pascal VOC format) to YOLO format. These commands will download a conversion script and then run it. The arguments for the script are 1) the path to the labels directory, 2) the path to the classes.txt labelmap file, and 3) a name for an output file. When you run the script, it will create a .txt file with data in YOLO format from every .xml file in the labels directory. curl -o convert_voc_yolo.py raw.githubusercontent.com/geaxgx/playing-card-detection/refs/heads/master/convert_voc_yolo.py python convert_voc_yolo.py path/to/labels/dir classes.txt converted.txt Good luck :| if that's complicated, Roboflow can convert data formats too.

[@krupaltisgaonkar238]

​@@EdjeElectronics I also have two directories: a train and a test folder, should I combine both of them into one and label it images and let google colab seperate it?

[@APie357]

hes alive

[@Kaixiang77441]

how did you know the candy weight? in YOLO

[@baguspandu_1ia244]

cuse me sir when ive upload the data.zip with the same format direct to the collab but it doesnt read any files, i try the gdrive too Created folder at /content/data/train/images. Created folder at /content/data/train/labels. Created folder at /content/data/validation/images. Created folder at /content/data/validation/labels. Number of image files: 0 Number of annotation files: 0 Images moving to train: 0 Images moving to validation: 0

[@baguspandu_1ia244]

nevermind i did it

[@ya_yo]

thanks for the tutorial!! would be great to have a tutorial to use these models in a iphone 🫣