#
Training
Once the dataset is ready, you can finally move on to training the models.
Trainings tab
and then click on Models
. As you can see, the collection of models looks similar to the catalog with our datasets. Create a new model using Add new model
.
Detection
and Classification
models.
#
Classification
Move on to the configuration of the classification model. Select Classification
.
New Accessories
. Continue by clicking Next
.
Classification Accessories
and select the TensorFlow 2.5.0 Keras
from the list of frameworks. Leave the Configure automatically
option on as this will set the relevant training parameters. Click Next
to proceed.
Keep in mind: Frameworks develop quite dynamically, so your version may differ from the one shown in the tutorial. If this is the case, use the latest version available on our website.
80%
.
Start training
button to start training.
Notifications
or Tasks
tab. Go back to the Models
to see the current status and progress.
Keep in mind: Due to hardware reasons, our app can only handle a certain number of trainings. It is possible that your training may end up in the waiting queue, but do not worry. Your models will not disappear when you leave the page. As soon as the machine is available again, your trainings will be completed.
Download model
button at the top.
Models
tab, click on the gear icon and select Re-train
or click on the Re-train
button at the top.
#
Object detection
To train a model for detection, follow the same steps as when configuring a model for classification. This time when adding a model click Detection
.
New Accessories
dataset and click Next
to proceed.
Detection Accessories
and select the Darknet
framework. For detection, you will use the Configure manually
option. Continue by clicking Next
.
yolov4.conv.137
in DARKNET
takes a very long time to pretrain. Use its smaller version yolov4-tiny.conv.29
so that you can use the model after only a few minutes. Leave the other parameters unchanged and click Next
.
merging categories
. Start the training by clicking on the Start training
button.
first 1000 batches
. Wait until you get accuracy.
74.94% Accuracy
. In this case you do not need a very accurate model, so you can stop the training. Click Stop
and select Stop with progress
from the list that appears, this will stop the training at the earliest opportunity. You can also stop the training by clicking Stop immediately
, but this will result in the loss of the created model.
Accuracy
is achieved or the Batch
count limit is reached.