深度学习领域faster-rcnn算法在小麦头目标检测（带数据集）-9、train-faster-rcnn-using-ker

https://www.kaggle.com/competitions/global-wheat-detection/data Dataset Description More details on the data acquisition and processes are available at https://arxiv.org/abs/2005.02162 What should I expect the data format to be? The data is images of wheat fields, with bounding boxes for each identified wheat head. Not all images include wheat heads / bounding boxes. The images were recorded in many locations around the world. The CSV data is simple - the image ID matches up with the filename of a given image, and the width and height of the image are included, along with a bounding box (see below). There is a row in train.csv for each bounding box. Not all images have bounding boxes. Most of the test set images are hidden. A small subset of test images has been included for your use in writing code. What am I predicting? You are attempting to predict bounding boxes around each wheat head in images that have them. If there are no wheat heads, you must predict no bounding boxes. Files train.csv - the training data sample_submission.csv - a sample submission file in the correct format train.zip - training images test.zip - test images Columns image_id - the unique image ID width, height - the width and height of the images bbox - a bounding box, formatted as a Python-style list of [xmin, ymin, width, height] etc.