Train Mask R-CNN for Image Segmentation (online free gpu)

Introduction

Welcome to this tutorial on creating a custom Mask R-CNN detector to identify specific objects, such as screwdrivers. In this step-by-step guide, we’ll walk through the simplest method to train a Mask R-CNN detector using Google Colab, which allows you to harness the power of a GPU without needing a powerful computer. The process can be broken down into three major steps: collecting images and preparing the dataset, training the custom detector on Google Colab, and using the trained model for object detection.

1. Collecting Images and Preparing the Dataset

The first and most crucial step is to create an adequate dataset. For this example, we will collect images of screwdrivers. You can take these pictures yourself using a smartphone or a camera; just ensure that your object of interest is present in the images.

Image Collection Guidelines:

• Variety: Position the screwdrivers differently each time you take a picture to introduce greater variety into the dataset.
• Background: Additional background objects in the image can help the model distinguish your target object from the background.
• Quantity: Aim for at least 40-50 images as a starting point to facilitate the annotation process.

Once you have collected your images, the next step is annotation, which will help inform the model where the screwdriver is located in each picture.

Image Annotation Process:

For annotation, we recommend using the open-source tool Makesense.ai. Follow these steps:

1. Go to the Makesense.ai website and get started by uploading your images.
2. Create a label for your screwdriver.
3. Choose Polygon as the type of annotation to be more precise about the object’s shape.
4. Draw polygons around all instances of screwdrivers in each image.
5. Once all images are annotated, export the annotations in the COCO JSON format.

After completing the annotation, retain two essential components for the next step:

• A folder containing all your images.
• The COCO JSON file with annotations.

2. Training the Custom Detector on Google Colab

In this section, we will use Google Colab to train our Mask R-CNN detector. Google Colab provides a free online environment with the option to enable a GPU for heavy compute tasks.

Step-by-Step Training Process:

1. Setup:

   • Open a Google Colab notebook that you can find through the shared link.
   • Enable GPU by going to Edit > Notebook Settings and selecting GPU as the hardware accelerator.

2. Install Dependencies:

   • Begin by installing necessary libraries and dependencies needed to implement Mask R-CNN.

3. Upload the Dataset:

   • Use the left pane in Colab to upload your images (zipped) and the annotations file.
   • Ensure paths to the files are correct in your code.

4. Running the Training:

   • The training consists of several epochs where the model learns from the dataset. Initially set to run over five epochs.
   • Monitor the log output to see training progress as the model refines its understanding of the screwdriver instances.

Testing the Model:

After the training is complete (or halfway done), use sample images from your dataset to evaluate the performance of your trained model. Code snippets are provided in the notebook for easy testing.

3. Using the Trained Model for Object Detection

Once your model is trained, you can use it to detect screwdrivers (or any other object you choose) in new images.

Steps to Run Object Detection:

1. Download the Trained Model: Once training is complete, download the .h5 file that contains the model.
2. Set Up Detection: Use a new Google Colab notebook to load your trained model and a test image for detection.
3. Evaluate Detection Output: The script will visualize detections, displaying polygons around detected screwdrivers in various colors.

In case you want to enhance your training process, you might consider investing in a pro version that supports multiple classes and advanced features.

Conclusion

If you have followed all the steps correctly, you should have a functional Mask R-CNN detector that accurately detects screwdrivers (or any selected object) in images. This technique can be expanded and adapted for various applications in visual recognition tasks.

Keywords

• Mask R-CNN
• Image Segmentation
• Object Detection
• Google Colab
• Dataset Annotation
• COCO JSON
• Free GPU

FAQ

Q1: What is Mask R-CNN?
A1: Mask R-CNN is a deep learning algorithm used for object detection and image segmentation. It can identify objects in an image and delineate them using polygons.

Q2: How can I collect images for training?
A2: You can take pictures with a smartphone or camera, ensuring to capture various angles and backgrounds for better dataset diversity.

Q3: Why should I use Google Colab for this project?
A3: Google Colab provides access to GPU resources for free, which significantly speeds up the training of models like Mask R-CNN.

Q4: What is the COCO JSON format?
A4: COCO JSON is a widely used format for storing image datasets and their annotations, particularly in the field of computer vision.

Q5: Can I train Mask R-CNN for multiple objects?
A5: Yes, but the basic demonstration presented here focuses on a single class for simplicity. More advanced implementations may include multiple classes.