Yolo v3 with TensorFlow 2
Posted May 24 by Rokas Balsys
Train YOLO v3 to detect custom objects (car license plate)
In this tutorial, I’m going to explain to you an easy way to train YOLO v3 on TensorFlow 2.x to detect a custom object even if you’re a beginner or even if you have no experience with coding.
1. Prepare the Image dataset
For this tutorial, I’m going to train YOLO to recognize a car license plate. To do that I would need to download a few hundred of these images:
Having the images is not enough, we also need to specify where the custom objects are located on the specific image.
For this operation we will need external software, we’ll use one of the most popular “LabelImg”. You can use it for Windows, Mac, or Linux: https://tzutalin.github.io/labelImg/ On GitHub it’s clearly explained how to use it.
Let’s set LabelImg for our dataset: Once we run it let’s click on “Open Dir”:
Select the folder where the images are located:
2. Now we’re ready to label the images
1. Click on “Create RectBox” (or W button in a keyboard):
2. Select the area where an object is located (in my case I select the license plate).
3. Add the label with the name of our object (In my case type license-plate and press Ok):
4. Then save labeled image.
At the end of this operation, in the same folder, we should see .xml file, for each image file with the same name.
I didn’t want to waste time labeling images just for this tutorial, so I searched for already labeled images with the same .xml labels. I found this dataset: https://github.com/RobertLucian/license-plate-dataset. I downloaded it and copied all images and labels to train and test files respectively.
3. Converting XML to Yolo v3
Now we must convert .xml labels to appropriate Yolo training format. If you didn’t clone my GitHub repository - do that. Then, open tools folder and open XML_to_YOLOv3.py script. At the beginning of code you should see the following code lines:
- data_dir is the location of our images folder, we set it to /custom_dataset/;
- Dataset_names_path is a location where generated classes path will be saved, we set it to model_data/license_plate_names.txt;
- Dataset_train is a location where generated train annotation path will be saved, we set it to model_data/Dataset_train.txt;
- Dataset_test is a location where generated test annotation path will be saved, we set it to model_data/Dataset_test.txt;
- the last line is is_subfolder, this is changed when we don't have any subfolders in train or test paths. In a previous tutorial, we were using subfolders when downloading the custom dataset, now we don't have subfolders, so we set it to is_subfolder = False.
This is how it should look fully changed:
... data_dir = '/custom_dataset/' Dataset_names_path = "model_data/license_plate_names.txt" Dataset_train = "model_data/license_plate_train.txt" Dataset_test = "model_data/license_plate_test.txt" is_subfolder = False ...
Now we just need to run XML_to_YOLOv3.py scripts and all these required files will be generated for us.
4. Change training configs.py file
Now when we have prepared our custom dataset and already generated annotations ant class files, we must change configurations to our needs. In the tools folder, you may see aconfigs.py file, open it.
We must change the following lines:
- TRAIN_CLASSES to "./model_data/license_plate_names.txt";
- TRAIN_ANNOT_PATH to "./model_data/license_plate_train.txt";
- TEST_ANNOT_PATH to "./model_data/license_plate_test.txt".
Make sure, that TRAIN_DATA_AUG is True and TRAIN_TRANSFER is also True. Train epoch and learning rate change according to your needs with respect to your custom dataset.
5. Train custom model!
This step is one of the simplest. Do the following steps:
- Go to the main folder;
- Open terminal;
- Write python train.py;
- The model keeps training, wait to finish it!
6. Evaluate your model
To check how your model is training, best is to check it on Tensorboard. So, open another new terminal is the same main folder and write: tensorboard --logdir=log and go to the following link: http://localhost:6006/. You should see similar results:
Above are graphs of all losses used in the training process, the most important is validate_loss/total_val:
Lower the value of validate_loss better the model is. In this example my curve is too correct to be true, but only because my dataset is small and lacks diversity. If you checked my previous tutorial, you saw that with the large dataset it's quite different.
7. Test custom model
Our custom model is saved in the checkpoints folder as yolov3_custom. To test this model, open the detection_custom.py script. And change your test image_path respectively. For this dataset I again took two random images from google, and tried to detect car license plate:
So, it was a quite simple and short tutorial, with detailed step by step explanations, how to train custom object detector. Even the beginner should be able to train a custom detector following this tutorial step by step. We could expand this tutorial, by extracting license plate number from detected objects, but this is not the goal of this tutorial series, maybe I'll come back to this task in the future.
Many of you may face a problem, that you don't have GPU on your computer, and it takes a lot of time to train custom model, especially if you have a large custom dataset. To solve this problem, in the next part I will show you how to train this custom model on the cloud with free GPU.