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We provide benchmark results of spatiotemporal prediction learning (STL) methods on popular traffic prediction datasets. More STL methods will be supported in the future. Issues and PRs are welcome! Visualization of GIF is released.
We provide visualization figures of various video prediction methods on various benchmarks. You can plot your own visualization with tested results (e.g., work_dirs/exp_name/saved) by vis_video.py. Note that --vis_dirs denotes visualize all experimental folders under the path, and --vis_channel can select the channel for visualization. For example, run plotting with the script:
Similar to Moving MNIST, we also provide the advanced version of MNIST, i.e., MFMNIST benchmark results, using $10\rightarrow 10$ frames prediction setting in configs/mfmnist.
Similar to Moving MNIST, we further design the advanced version of MNIST with complex backgrounds from CIFAR-10, i.e., MMNIST-CIFAR benchmark, using $10\rightarrow 10$ frames prediction setting in configs/mmnist_cifar.