The Simplest Way to Train Object Detection Models on Custom Datasets

ByJordan Brown

Object detection is a fundamental task in computer vision that involves identifying and localizing objects of interest within an image. It has wide-ranging applications including autonomous vehicles, robot navigation, surveillance systems, medical imaging, and more.

While pre-trained models are available for common object categories, many real-world use cases require detecting custom objects. However, training object detection models from scratch on custom datasets has traditionally been a complex and time-consuming process.

Fortunately, high-level libraries like Detecto have emerged to drastically simplify custom object detection. Built on top of PyTorch, Detecto provides an intuitive interface for training models with just a few lines of code. In this guide, we‘ll walk through how to use Detecto to train a custom object detector, step by step.

Collecting and Labeling a Custom Dataset

The first step is to gather a collection of images containing the objects you want to detect. There are several ways to source relevant images, such as:

  • Manually capturing photos with different cameras, angles, lighting conditions, backgrounds, etc.
  • Leveraging online resources like Google Images or Open Images Dataset
  • Generating synthetic images via data augmentation or simulation

Aim to collect at least 100-1000 representative images to train a robust model, depending on the complexity of your task. The more diverse the dataset, the better the model will generalize.

Once you have the raw images, they need to be annotated with bounding boxes and class labels around each object instance. Various labeling tools are available to streamline this process, including:

  • LabelImg: Lightweight graphical image annotation tool
  • CVAT: Web-based collaborative annotation platform
  • RectLabel: Paid but fully-featured desktop annotation software
  • Labelbox: Cloud-based data labeling service with team collaboration features

Export the annotations in a standard format like PASCAL VOC or COCO. Clearly define your label schema from the start and be consistent. Split the labeled dataset into train, validation and test sets.

Setting Up the Environment

With your custom dataset prepared, it‘s time to set up Detecto. Create a new Python virtual environment and install the necessary dependencies:

!pip install torch detecto matplotlib

Detecto requires PyTorch 1.7.0+. It can leverage a GPU if available to significantly speed up training and inference. Verify your configuration:

import torch
print(torch.cuda.is_available())

If CUDA support isn‘t detected, ensure you have a compatible NVIDIA GPU and drivers installed. Then launch a Jupyter notebook or Python script to start developing your custom object detector.

Loading and Visualizing the Dataset

Detecto provides convenient utilities for loading labeled datasets in the standard VOC XML format. Assuming your dataset is structured like:

dataset/
    train/
        images/
            img001.jpg
            img002.jpg
            ...
        annotations/
            img001.xml 
            img002.xml
            ...
    test/
        images/
            img101.jpg
            ... 
        annotations/
            img101.xml
            ...

You can load it into Detecto Datasets with:

from detecto import core, utils, visualize

train_dataset = core.Dataset(‘dataset/train‘)
test_dataset = core.Dataset(‘dataset/test‘)

Detecto handles all the data parsing and preprocessing under the hood. You can easily visualize samples from the loaded dataset:

image, target = train_dataset[0] 
visualize.show_labeled_image(image, target[‘boxes‘], target[‘labels‘])

This plots the image with bounding boxes and labels overlaid. Inspect your dataset to verify the annotations are loaded correctly.

Defining the Model

Next, define the object detection model architecture. Detecto uses Faster R-CNN with a ResNet-50 backbone by default, which achieves strong performance on challenging benchmarks like COCO.

To instantiate the model, simply provide a list of your custom class labels:

model = core.Model([‘dog‘, ‘cat‘, ‘person‘])

Detecto initializes the model with weights pre-trained on ImageNet for transfer learning, which helps it converge faster while requiring less data than training from random initialization.

You can optionally specify a different base architecture:

model = core.Model([‘dog‘, ‘cat‘, ‘person‘], base_model=‘fasterrcnn_resnet101‘)

The options include FasterRCNN+ResNet and RetinaNet+ResNet variants. Larger backbones yield better accuracy but slower training/inference speed. ResNet-50 offers a good balance for most applications.

Training the Detector

Now the exciting part – training the object detector on your custom data! First, create a DataLoader to iterate over batches of images and annotations:

train_loader = core.DataLoader(train_dataset, batch_size=2, shuffle=True)

The batch size determines how many images are processed in parallel. Larger batch sizes offer higher throughput but require more GPU memory. Shuffle the data to reduce overfitting.

Then initiate the training loop by calling fit() on the model:

losses = model.fit(train_loader, test_dataset, epochs=10, lr_step_size=5, learning_rate=0.01, verbose=True)

This trains the model for 10 epochs using a learning rate of 0.01 and prints progress to the console. The lr_step_size parameter decays the learning rate every 5 epochs, which can help it converge more stably.

Training may take minutes to hours depending on your dataset size and hardware. Monitor the validation loss and average precision metrics logged after each epoch to gauge progress. The model weights are saved whenever validation performance improves.

You can visualize the training loss curve after fitting:

import matplotlib.pyplot as plt

plt.plot(losses)
plt.xlabel(‘Epoch‘)
plt.ylabel(‘Loss‘) 
plt.show()

Well-trained object detection models should achieve validation losses under 0.05 and mean average precision (mAP) over 0.9. If performance plateaus early, try:

  • Adding more training data
  • Data augmentation to add scale/shift/rotation variants
  • Tuning hyperparameters like learning rate, momentum, regularization
  • Increasing model capacity with a larger backbone
  • Adjusting anchor box sizes to better match object shapes

Feel free to experiment and iterate until you‘re satisfied with the results.

Saving and Loading Models

Once your custom object detection model is trained, save the final weights for later use:

model.save(‘model_weights.pth‘)

You can reload the model anytime by calling:

model = core.Model.load(‘model_weights.pth‘, [‘dog‘, ‘cat‘, ‘person‘])

Be sure to provide the same list of class labels the model was originally trained with. Loading a pre-trained model is useful for performing inference on new data or deploying into an application.

Running Inference

Finally, run the trained object detector on test images to locate objects of interest! Load an image and call predict():

image = utils.read_image(‘test.jpg‘)
predictions = model.predict(image)

labels, boxes, scores = predictions
print(labels, boxes, scores)

This outputs the predicted labels, bounding boxes, and confidence scores for each detected object. The number of predictions can be configured via the max_predictions argument.

To visualize the detections, use show_labeled_image():

visualize.show_labeled_image(image, boxes, labels)

By default, it draws boxes around all detections. To filter out low-confidence noise detections, you can apply a score threshold:

thresh = 0.7
filtered_indices = np.where(scores > thresh)

filtered_boxes = boxes[filtered_indices]   
filtered_labels = [labels[i] for i in filtered_indices]

visualize.show_labeled_image(image, filtered_boxes, filtered_labels)

Play with the threshold to trade off between precision and recall – higher values give fewer but more accurate detections.

And that‘s it! You now have a powerful custom object detector you can apply to new images. Embed it in your own applications to automatically identify relevant objects.

Going Further

While Detecto makes custom object detection incredibly straightforward, there are still challenges to navigate and tradeoffs to consider when deploying models into production settings.

Key areas to keep in mind:

  • Optimizing for inference speed and memory footprint, especially on edge devices
  • Handling crowded scenes with many overlapping objects
  • Accounting for domain shift if deploying into very different environments than trained on
  • Continuously collecting and labeling new data to improve and maintain performance over time
  • Explaining model predictions and failure modes to users

Relative to other deep learning domains, object detection is still rapidly evolving. There are opportunities to improve architectures, loss functions, training recipes, data augmentation, and more.

Some promising research directions include:

  • Anchor-free detectors to avoid manually tuning anchor hyperparameters
  • Self-supervised and unsupervised pre-training to learn better feature representations
  • Few-shot object detection adapting to novel classes with limited labeled data
  • Combining multiple sensor modalities like RGB, thermal, depth, etc.
  • 3D object detection for point clouds
  • Detecting irregularly shaped objects with pixel-wise segmentation
  • Real-time video object detection and tracking

To go deeper into the theory and practice of object detection, check out these resources:

Conclusion

Object detection is a powerful computer vision technique that has been historically challenging for beginners and even experienced practitioners to apply to their own custom tasks and datasets.

However, the Detecto library drastically lowers the barriers to training your own custom object detection model by providing a batteries-included, easy-to-use interface on top of the popular PyTorch framework.

As you‘ve seen in this step-by-step guide, you can go from a folder of labeled images to a working object detector in just a few lines of code, without sacrificing much flexibility or performance.

Whether you‘re building a robot to navigate around obstacles, an app to recognize products on store shelves, or a biologist identifying cell types under a microscope, Detecto offers a productive workflow to rapidly prototype solutions to your object detection needs.

Advanced users can still access the full power of PyTorch to customize models, training loops, losses and more. The Detecto source code is a clean and readable reference implementation to study.

With continued improvement of models, tools and best practices by the research community and open source ecosystem, the future of object detection is bright. It will be exciting to see how this fundamental building block powers the next generation of intelligent visual systems.

Hopefully this guide has inspired you to experiment with object detection yourself and discover creative ways to perceive and interact with the visual world in your own projects. Happy detecting!

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