The FastRCNNPredictor class in the Python torchvision.models.detection.faster_rcnn module is used for post-processing and predicting object detections using the Faster R-CNN algorithm. It takes the output features from the region proposal network and applies a fully connected layer followed by the softmax function to generate class probabilities for each bounding box proposal. Additionally, it also predicts the offset values for refining the bounding box coordinates. Overall, the FastRCNNPredictor helps in predicting the class labels and refined localization of objects in an image using the Faster R-CNN model.
Python FastRCNNPredictor - 30 examples found. These are the top rated real world Python examples of torchvision.models.detection.faster_rcnn.FastRCNNPredictor extracted from open source projects. You can rate examples to help us improve the quality of examples.