如何基于自定义数据集训练Mask R-CNN Inception ResNet V2实例分割模型

Hey there! I get that navigating the instance segmentation training workflow can feel overwhelming, even with official docs. Let’s break down your questions clearly:

1. Do I need to provide both bounding box coordinates and mask.png files? #

Absolutely. Mask RCNN is built to handle both object detection (which depends on bounding boxes) and instance segmentation (which requires pixel-level masks). While you could technically derive bounding boxes from masks, providing explicit, accurate bounding boxes helps the model converge faster and more reliably. The model uses bounding boxes to first focus on relevant regions, then refines the segmentation within those areas. So yes, you should prepare both for your custom dataset.

2. How to convert instance segmentation mask data into TFRecord files? #

TFRecord is TensorFlow’s optimized format for efficient data loading. Here’s a practical step-by-step workflow:

• First, organize your dataset: have a folder of input images, corresponding mask files (either one mask per image with unique pixel IDs for each instance, or separate mask files per instance), and a metadata file (like CSV/JSON) mapping each image to its bounding boxes and mask paths.
• Adapt the scripts from the TensorFlow Object Detection repo. For example, start with the create_pascal_tf_record.py script (originally for Pascal VOC) and modify it to handle mask data. Key steps in the script will include:
  • Loading each image and its associated masks.
  • For each instance, normalizing bounding box coordinates to the [0,1] range and converting masks into binary/instance-specific tensors.
  • Packing all this data into TFExample protobufs, then writing them to the TFRecord file.
• Note: If using a single mask file per image with instance IDs, you’ll need to extract each instance’s mask by filtering pixels with the corresponding ID. If using separate mask files per instance, load each one individually for the same image.

3. What tools can I use to annotate both bounding boxes and masks together? #

You’re right that some tools seem limited, but there are solid options that support both:

• CVAT (Computer Vision Annotation Tool): A robust open-source tool that lets you draw polygon masks (which auto-generate bounding boxes) and explicit bounding boxes. You can export annotations in COCO JSON format, which includes both bbox and mask data.
• VGG Image Annotator (VIA): A lightweight browser-based tool where you can draw bounding boxes and polygon masks directly on images. It exports annotations in JSON, which you can parse to extract both bbox coordinates and mask polygons (convert polygons to pixel masks later with OpenCV or PIL if needed).
• LabelMe: Wait, you mentioned it only provides one type—but when you draw a polygon mask in LabelMe, the exported JSON includes both the polygon coordinates (for mask generation) and the bounding box of the polygon. You can write a quick script to extract the bbox from the polygon’s min/max x/y values, and convert the polygon to a pixel mask easily.

Content of the question comes from stack exchange, question author aashish garg