Train TensorFlow Lite Model for Custom Object (License Plate) Detection with Custom Dataset
If you want to build a custom object detection model using TensorFlow Lite for detecting license plates, you will need to follow these steps:
- Collect and Label Your Dataset: Gather a dataset of images that contain license plates. Make sure to label each license plate in the images with bounding boxes using annotation tools like LabelImg.
- Convert Dataset to TensorFlow Record Format: Convert your labeled dataset to TensorFlow Record format, which is compatible with TensorFlow’s Object Detection API. This step is necessary for training your model.
- Configure Model Pipeline Config: Set up your model’s pipeline configuration file by specifying the architecture of the model, the number of classes (in this case, 1 for license plates), and other parameters like input size, batch size, and learning rate.
- Start Training: Start training your custom object detection model using TensorFlow’s Object Detection API. You can train your model on a cloud platform like Google Colab or on your local machine if you have the necessary hardware resources.
- Evaluate and Export Model: Once your model has been trained, evaluate its performance on a validation set to ensure it can accurately detect license plates. Export the trained model as a TensorFlow Lite model for deployment on edge devices.
- Integrate TensorFlow Lite Model: Finally, integrate your TensorFlow Lite model into your application or project for license plate detection. You can use TensorFlow Lite’s Python API to run inference on images or videos in real-time.
By following these steps, you can train a custom TensorFlow Lite model for license plate detection with your own custom dataset. This model can be deployed on edge devices for real-time object detection applications.
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sir is this compatible in flutter?
my model takes so much time to train (hours..). is that normal or can there be a problem ?
I can't connect to the gpu while training the model. Even though TPU4 is selected, training takes place via the CPU.
hi, could you help me? i'm already finish training my own dataset (big thanks) but i've got error during running your LicenseDetector.py the messages is "from tensorflow.lite.python.interpreter import Interpreter
ModuleNotFoundError: No module named 'tensorflow.lite.python.interpreter'"
may you have any solution to solve this problem, thank you. keep it up 🙌
hi, is a really nice video, its working, but i have a question, my dataset is large, when the time of google colab is done, how can start the train again but from the las checkpoint?
How to create our own data set and use that ?
Hello Brother
I am getting error in this section and unable to proceed further , could you please help on this?
pipeline_fname = '/content/models/mymodel/' + base_pipeline_file
fine_tune_checkpoint = '/content/models/mymodel/' + model_name + '/checkpoint/ckpt-0'
def get_num_classes(pbtxt_fname):
from object_detection.utils import label_map_util
label_map = label_map_util.load_labelmap(pbtxt_fname)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=90, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
return len(category_index.keys())
num_classes = get_num_classes(label_map_pbtxt_fname)
print('Total classes:', num_classes)
Great tutorial! Thanks!
I got one odd error running your code: It didn't generate the labelmap.pbtext file for some reason. I just created it manually, but would love to know what I'm doing wrong for next time. My copy of the notebook is here: https://colab.research.google.com/drive/1qM9SOHhkZk4d5vGUXX_PUdayKIxTTtBB?usp=sharing
From where i get my key in roboflow