Welcome to your guide on implementing the cutting-edge model for satellite image time series classification using Pixel-Set Encoders and Temporal Self-Attention, as presented in the 2020 CVPR conference.
What You Need
- Python 3.6 or above
- PyTorch and torchnet
- Numpy, Pandas, and Sklearn
- Linux-based OS (recommended: Ubuntu 18.04.1 LTS)
Getting Started
The project broadly involves setting up datasets, running training scripts, and making predictions using a pre-trained model. Here’s how you can do that:
Step 1: Download Datasets
You can access various datasets needed for this project:
Step 2: Set Up The Code Structure
After you’ve downloaded the datasets, clone the repository and navigate to the models folder where you will find the implementations of the Pixel-Set Encoder (PSE), Temporal Attention Encoder (TAE), and combined PSE+TAE architectures.
Step 3: Training the Model
To train the PSE+TAE model, use the following command:
python train.py --dataset_folder /path/to/Pixel-Set/datasetHere, just replace “/path/to/Pixel-Set/dataset” with the actual path to your dataset folder.
Step 4: Making Predictions
Once your model is trained, you can use the pre-trained weights to make predictions on your dataset or your own formatted dataset:
python inference.py --weight_dir /path/to/weightsAgain, replace “/path/to/weights” with the path to your pre-trained weights folder.
Understanding the Code: An Analogy
Think of the PSE+TAE model as a diligent librarian organizing a vast library of books (satellite images) in a uniquely efficient way. The Pixel-Set Encoder acts as the librarian, categorizing books (pixels) that are not necessarily in a specific order but need to be sorted by topic (features). This allows for a quicker lookup of references and ensures logical groupings without the constraints of traditional cataloging methods. The Temporal Self-Attention layer, on the other hand, allows the librarian to understand the context of the books over time—making connections between different texts that may become more relevant as situations change (temporal features). This means, rather than looking through each book individually, the librarian can quickly find relevant information—saving time and memory, akin to enjoying a smooth, organized reading experience!
Troubleshooting
In case you run into trouble, consider the following options:
- Ensure that your data structure matches the specified format in the README.
- Check that all dependencies are installed correctly.
- Look through the training logs for errors or warnings that could point you to the source of the issue.
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Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.