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title: PDS: 行人检测及分割
author: librah
date: 2020/5/5
---
Object Detection is a common Computer Vision problem which deals with identifying and locating object of certain classes in the image. Interpreting the object localization in various ways, including creating a bounding box around the object or marking every pixel in the image which contains the object (called segmentation).
Image segmentation creates a pixel-wise mask for each object in the image. This technique gives us a far more granular understanding of the object(s) in the image.
The model is Mask R-CNN, which is based on top of Faster R-CNN. Mask R-CNN adds an extra branch into Faster R-CNN, which also predicts segmentation masks for each instance.
networks: Mask R-CNN, based on torchvision.models.detection. We use the FastRCNNPredictor to detection boxes and scores of objects, and use the MaskRCNNPredictor to object segmentation.
utils: some official application tools.
dataloader.py: define a custom dataset for Pedestrian.
experiments.py: train model, predict and show the results.
[1] https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html