Object detection and instance segmentation are crucial tasks in computer vision, with numerous applications ranging from self-driving cars to medical image analysis. However, a significant challenge is a need for large labeled datasets to train accurate models. Labeling datasets manually can be a tedious and time-consuming task, often requiring significant effort and resources.
To address this challenge, model-assisted labeling has emerged as a powerful technique that can save time and money by reducing the number of manual annotations required. In this blog post, we will explore how model-assisted labeling works and how it can accelerate the labeling process for both object detection and instance segmentation. As an example, we will demonstrate how you can use the trainYOLO platform to easily apply this method to your YOLOv8 object detection or instance segmentation training.
Model-assisted labeling is a process that uses a (pre-)trained machine learning model to generate annotations for a dataset automatically. Specifically, the model is used to predict the labels of the objects in the dataset, and these predictions are then used as a starting point for the manual labeling process. The annotations generated by the model only need to be refined or corrected by human annotators, reducing the total number of annotations required as opposed to labeling from scratch.
For example, in object detection tasks, a pre-trained model can be used to generate bounding boxes around the objects in an image. These bounding boxes can then be used as a starting point for the manual labeling process, with human annotators refining and correcting the locations of the boxes as necessary. This approach can save significant time and effort compared to manual labeling from scratch. As an example, see the difference between manually labeling an image of pollen versus using a model-assisted method:
One of the key benefits of model-assisted labeling is that it enables a feedback loop between training the model and labeling more images. As the model gets better at generating initial annotations, the amount of manual work required decreases, allowing more time to be spent on training and improving the model. This cycle can be repeated multiple times, with each iteration resulting in a more accurate model and fewer manual annotations required. By continually improving both the model and the dataset, the overall accuracy of the object detection system can be significantly improved. Therefore, it is important to keep iterating and fine-tuning the model and dataset to achieve the best results possible.
The trainYOLO platform streamlines the process of model-assisted labeling for object detection and instance segmentation algorithms like YOLOv8 or YOLOv5. After each training iteration, you can upload your model to the platform and generate predictions with a click of a button. No need to upload predictions yourself.
As an example, let’s take a look at the steps to train a YOLOv8 pollen detector using the model-assisted labeling approach. For a more detailed guide about how to upload, label and train algorithms like YOLOv5 or YOLOv8 using trainYOLO, take a look at our other posts: YOLOv5 object detection, YOLOv8 object detection, YOLOv8 instance segmentation.
1. Label an initial batch manually
To kickstart the process of model-assisted labeling, we begin by manually labeling the first batch of images. In this stage, it is crucial to choose a diverse set of images to improve the trained model's generalization. Also, the number of images that need to be labeled manually in this first batch varies depending on the task. If the images have many objects, fewer are required, and vice versa. In any case, the initial trained model's performance will indicate if more manual labeling is necessary.
2. Train an initial model
Once the first batch of images is labeled, it's time to train the initial model. With trainYOLO's preconfigured Colab notebooks, training a YOLOv5 or YOLOv8 object detection or instance segmentation model is straightforward (see here for a detailed guide on how to start training). All you need to do is fill in your API key and Project name to start the training. Once finished, the model and its metrics are uploaded to our platform, where it is automatically deployed for use in model-assisted labeling. In our case, we labeled 50 images (comprising 1221 pollen) and reach the following accuracy:
With a score of 96.4 mAP, we have a perfect starting point for our model-assisted labeling. Looking back, we could have started out with a smaller batch of images.
3. Model-assisted labeling
Now that we have an initial model trained, we can start to utilize it as a labeling assistant. As the model is automatically deployed on trainYOLO, it enables us to generate predictions with a click of a button (the magic paintbrush). Note that the labeling speedup is dependent on the performance of the initial model , but it is expected to improve significantly with additional training iterations.
As previously mentioned, one of the significant advantages of model-assisted labeling is the feedback loop it creates between training the model and labeling more images. Therefore, it's recommended to train a new model each time we label another set of images. This approach enhances the prediction’s accuracy and accelerates the labeling process. A win-win for all.
Model-assisted labeling is a powerful technique for accelerating the labeling process for object detection tasks. By using the model to jump-start the initial annotations, model-assisted labeling can save significant time and money, while also improving the accuracy of annotations. To make the most of model-assisted labeling, it's important to focus on challenging images and correct the annotations generated by the model. By following these tips, you can effectively use model-assisted labeling to label datasets faster and more accurately.
In conclusion, model-assisted labeling is an important technique that can help overcome the challenges of manual labeling in object detection tasks. By leveraging the predictions of trained models combined with human expertise, model-assisted labeling can accelerate the labeling process while improving the accuracy of annotations. As computer vision applications continue to grow in importance and turnover rates continue to rise, model-assisted labeling is likely to become an increasingly valuable tool.