We uploaded the images of the battery.
Now let's label those images, click label myself creating job loading images.
And now you see.
Guys, this is a tip here to help you build a good model.
This is not a good model because uh, the the annotation and the box is drawing around the head and
the helmet while the correct one is drawing just on the helmet.
So you see, guys, this is our annotation, um, annotation tip click get Started.
And here let's start annotating.
You see guys 63 images you can create uh, more okay.
You see this is the smart select using Sam A3.
Click finish.
And you see guys this is the object.
This is the battery.
You can name it as battery okay.
Save.
And it's saved.
So you see guys this is the first one.
Now let's annotate the second one.
You can draw the box like this drawing like this.
Or you can select using the Sam three, click finish.
You see guys this is the battery in uh in purple and yellow objects.
You can delete the object, the entire object or the battery.
So battery is not detected.
So you need to uh to delete it.
You can undo using this going to layers delete.
And here select delete delete if you missed anything.
If you uh if you annotate it by by mistake here we have use this smart Select.
You can also use the bounding box tool.
So I'm going to draw like this.
This is the battery and I name it as battery.
You can select this class battery okay.
The image number three.
This is the battery.
Click save.
So as you see guys there are a lot of tools that uh help you uh smart selecting the image.
You see guys this is Sam three and this is the battery you see.
Or you can use the standard bounding box or the polygon or the brush tool and other tools.
So the most important thing is bounding box.
Using the shortcut is B and the smart select is S okay.
Bounding box I'm drawing drawing this battery save.
Next this is the box and save next this is the battery and so on okay.
So whenever you finish 63 images click on this Add images to data set.
And here we go I labeled this all the images 63 images.
You can see all the images are being labeled correctly using the bounding boxes.
So add images to data set.
Click okay and labeled images to add 63 remaining batch images.
Zero are labeled every image.
Here we have train validation and test.
We need to split and make a distribution of our images.
So here you can split images between train and test.
Add all images to training.
Set all images to validate the validation set and all images to testing set.
You can, um, use existing values or split images between training, validation and test.
70% for training is good, valid 20% and test 10% is good.
We have to train 44 images valid 613 images and test six images.
All those values are very important because we need to get a 70% for training, 20% for validation.
You can increase the number for, for example, 5,075% for training and validation, 15% and testing.
This is good.
Add 63 images and congratulations guys.
This is our data set okay.
So this is the data set.
We can select the train.
Those are the images that are selected for training those images for validation and those images for
testing okay.
So select all and here is our data set.