Original subtitles

Okay, then we will do training of these seven on custom objects next.

So the dataset is set up and the configuration file is created.

If are doing training, we have to download Pre-trained weights for transfer learning.

Visit the following repositories.

Scroll down after the download YOLO of seven Training from the Transfer Learning section, click on

the following link.

Wait until the download is finished.

When you finish, navigate to the weight file in the downloads folder.

After that, move the words to the all of 70 plus route folder.

Click the file, then press control X.

Place in the all of seven GP use route followed by pressing control fee.

But before doing the training, we will explain some arguments that can be used.

First, there is the worker's argument.

This argument is the number of processes that generate parties in parallel.

This is an example of its application.

Next is the bed size argument.

This argument is the number of images processed before updating the model.

This is an example of its application.

If the CPU used for training has relatively small set workers to zero and reduce the bad size.

Makes this the device argument.

This argument is kill the device.

This is an example of its application.

If using Cipro, you write Cipro, you and device.

Next is the data argument.

This argument is a data file that contains the number of classes that Cipro and class name.

This is an example of its application.

Next up is the IMT argument.

This argument is the size of the image to be trained.

This is an example of its application.

Next is the CFD argument.

This argument is a configuration file.

This is an example of its application.

Next week's argument.

This argument is a wedge file that is uses Pre-trained words for transform learning.

This is an example of its application.

Next name argument.

This argument is the name of the model to be trained.

This is an example of its application.

Next hip argument.

This argument is a YOLO of seven separate parameter.

This is an example of its application.

Next is the epochs argument.

This argument is the number of times the learning algorithm will work to process the entire dataset.

This is an example of its application.

The first step in performing the training is to launch the Anaconda prompt.

Press the Windows button, then enter Anaconda.

Click the Anaconda prompt.

Then activate the all of seven CPU environment using the command.

Activate yolo 574 for and V.

Press internal.

Then navigate to the YOLO seven zip use route folder.

We will do training with the face mask dataset, the Nvidia GeForce GTX 1650 TI with four gigabytes

of GPU.

RAM is used for training used to command below fight and train dog p y.

In workers set zero.

In the bedside we write for.

You can increase the bed size value if you're using a GPU with more RAM.

On device zero.

In the data, right?

The data file that was previously created.

At IMDB, we read 640.

In the CFG write the configuration file that was previously created.

In width.

We use yolo v seven training as initial weights.

In the name we write YOLO.

Five seven Face mask.

In hit use helps create custom file in the data folder.

In epochs.

We write 300.

Press enter to start training.

The following is the training process.

It's a.

We'll calculate the MLP to get the best weights.

If you want to stop training before the specified epochs, you can press control C.

The future training results once Windows Explorer and navigate to the YOLO v seven Use route folder.

The training results are stored in the runs train.

Model name.

The training without weights file is stored in the weights folder.

That's not pretty is the words with the highest MLP value last the p t is the weights of the last epoch.

Next we can see the training graph using the tensor board.

Use the command below.

Sensible.

That's.

That's floor zero.

One strain.

First internal.

Copy the following link by blocking like this, then right click.

Open your browser.

Taste the cup and link.

The following is a graph of the training results.

For performance.

You can pay attention to the map graph of mean average precision, the high of the better.

In the next video, we will explain how to continue training.

See you then.