Instance Segmentation Using Mask R-CNN on Custom Dataset

Instance segmentation is a computer vision task that involves detecting and segmenting multiple objects within an image. Mask R-CNN is a popular deep learning model that can perform instance segmentation with high accuracy. In this article, we will discuss how to use Mask R-CNN to perform instance segmentation on a custom dataset.

Creating a Custom Dataset

In order to train Mask R-CNN on a custom dataset, you first need to create a dataset containing images and their corresponding annotations. The annotations include the bounding boxes and class labels of the objects in the images. You can use tools like LabelImg or VIA to annotate your images and generate the necessary files.

Training Mask R-CNN

Once you have your custom dataset ready, you can start training Mask R-CNN. You can either train the model from scratch or fine-tune a pre-trained model on your dataset. Training a deep learning model like Mask R-CNN can be computationally intensive, so make sure you have access to a GPU to speed up the process.

Evaluating the Model

After training the model, you can evaluate its performance on a separate validation set. You can use metrics like mean Average Precision (mAP) to measure the accuracy of the model in detecting and segmenting objects. Fine-tune the model based on the evaluation results to improve its performance.

Testing the Model

Once you are satisfied with the performance of the model, you can use it to perform instance segmentation on new images. The model will detect and segment objects in the images, providing you with accurate masks for each object.

Conclusion

Instance segmentation using Mask R-CNN on a custom dataset can be a powerful tool for various computer vision tasks. By following the steps outlined in this article, you can train and deploy a highly accurate instance segmentation model on your own dataset.