Link of dataset which I used in code
The goal of this task is to retrieve all images which show direct evidence of a flooding event from social media streams, independently of a particular event. The objective is to design a system/algorithm that given any collection of multimedia images and their metadata (e.g., YFCC100M, Twitter, Wikipedia, news articles) is able to identify those images that are related to a flooding event. Please note, that only those images which convey an evidence of a flooding event will be considered as True Positives. Specifically, we define images showing unexpected high water levels in industrial, residential, commercial and agricultural areas“ as images providing evidence of a flooding event. The main challenges of this task are the proper discrimination of the water level in different areas (e.g., images showing a lake vs. showing high water at a street) as well as the consideration of different types of flooding events (e.g., coastal flooding, river flooding, pluvial flooding).
For every author in the dataset, submission files should contain two columns: id and label. The file should contain a header and have the following format:
id,label
1,1
2,0
Convert the raw image to matrix format File here
Load the matrix image dataset
I use ConvNeXtXLarge and add some layers for my code.
I use checkpoint to save the model which has best accuracy. I use reduce_lr to reduce learning rate in a period which does not improve the accuracy.
Load the model (I use general model, you can use the best_checkpoint_model instead) to use. After that, I predict all test images and save it into an csv file to submit.