Autodistill: Train YOLOv8 with ZERO Annotations

Description:

Autodistill is a ground-breaking tool revolutionizing the world of computer vision! In this video, we will show you how to use a new library to train a YOLOv8 model to detect bottles moving on a conveyor line. Yes, that's right - zero annotation hours are required! We dive deep into Autodistill's functionality, covering topics from setting up your Python environment and preparing your images, to the thrilling automatic annotation of images.

What makes Autodistill a game-changer is its ability to distill knowledge from large foundational models like GroundedSAM, transferring this knowledge into highly-optimized computer vision models. We're ecstatic to showcase how this library can turn hours of tedious manual annotation into a fully automated process, without compromising the accuracy of your models.

Chapters:

00:00 Autodistill Overview
02:03 Project Overview
03:16 Setup Python Environment
04:46 Prepare Images
06:22 Autoannotate Images
07:50 Train YOLOv8 Model
08:28 Run Inference on Video
09:16 Conclusion

Resources:

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[sebbecht]

Well done Roboflow, I am glad a distillation video worked out, i wasnt disappointed!!

[jingjungpractice]

You're the best. I love you. You've made a significant impact on computer vision tasks.

[abdshomad]

Small request for next video: data collection -> autolabel -> load to roboflow for manual correction -> train -> compare accuracy with today's result

[maheshagodekere]

Thanks for sharing the autodistill library. This is awesome. The era of labelling is gone!

[rachealcr]

Great Video! Just wonder after deploying to Roboflow, how to verify the roboflow evaluation metrics like mAP, Precision and Recall with the one in google colab?

[gisanimrahma]

hi, thank you for the helpful information. truly appreciate it. i have a question, is there any way for the autodistill to only return the rectangle bounding box value and not a polygon?

[hajerbettayeb]

Thank you for sharing. I have question about results of YOLOv8 model, after the training of the model it results in a 3-dimensional confusion matrix taking the background as a class knowing that I have a binary classification my project is classification of preforms whether it is defect or not. What can be the raisaon of appearance of this class "backgroud" and how I can solve it ? if you help me I would be grateful.

[exoticcoder]

Thanks Mate ! May I know the reason why are you choosing to use Yolov8 but not Yolo-NAS ?

[tlchannel]

this is for any object and i only need to load the image and ontology or is it only the objects contained in yolo coconame that can label

[PrakashKeshari-igpk]

hi, i need a help, i need to detect shape like circle, triangle, rectangle in image and check all shaope are correctly drown are not, and guide me I try all shape detection model but unable tom find solution,

[evanshlom]

Combine this with yolo as-one and it’s pretty easy to see how different parts of a larger process can be combined into an overall framework. Could be a pipeline framework, could be something else. Probably already being worked on if not released

[Tolazytoedit]

I am working on project to detect a car model in real time i train 5 images but i didn't know whether they are trained well or not if they are than how to keep that data and detect car model in real time i so confuse how does this happen when i install yolo v8 it already has some in-built data detection in realtime than how can i put my own custom data in that classes

[aymenbenammar]

Great video ! It helped me annotate my dataset from scratch
Question : does the G-SAM annotation make a big difference compared to annotation with G-DINO ( without segmentation) ?

[kobic]

looks promising! so if I understand correctly - there are two phases: (1) auto-label an image dataset, something that you already showed in the G-SAM previous video (2) split the dataset to test/train/val and build a CV model with YOLO-8
q1: since auto-labeling is not 100% fullproof, what about the human in the loop?
q2: what if the base model doesn't recognize the labels (name of specific boats I have in a marine dataset), how can I fine-tune it? so the model will find a boat, but will also "know" the brand of the boat

[datatekjung]

Thanks for so much useful information, BTW is it also possible to classify drones without any labeling like this video?

[VintageVibes]

Nicely explained @Piotr and awesome content @Roboflow

[TUSHARGOPALKA-njjx]

Is there a smaller version of Yolov8 that can be trained on the ade-20k dataset?

[youssefkhaled]

Thnx for the content, Can I know how to open webcam in yolov7 method in colab I try hard but I get nothing thanks again.

[khalidalsinan]

why aren't the bottles and caps segmented in the video?

[jojkxhx]

Is it possible to make your own custom base model like Grounding DINO?