MovingMnist数据集常用于哪些时空序列预测任务? 前言 毋庸置疑在做时空序列模型的时候,oving数据集,或者说标准的数据集是必要的 这篇文章我们主要介绍MovingMnist数据集,做这个方向的research是逃不过这个数据集的使用的 一、 Address 地址:http://www.cs.toronto.edu/~nitish/unsupervised_video/ 这个数据集主要是在 ...
Moving MNIST [782Mb] contains 10,000 sequences each of length 20 showing 2 digits moving in a 64 x 64 frame.The results in the updated arxiv paper use this test set to report numbers. For future prediction, the metric is cross entropy loss for predicting the last 10 frames for each ...
时空预测学习是一个拥有广泛应用场景的领域,比如天气预测,交通流预测,降水预测,自动驾驶,人体运动预测等。 提起时空预测,不得不提到经典模型ConvLSTM和最经典的benchmark moving mnist,在ConvLSTM时代,对于Moving MNIST的预测存在肉眼可见的伪...
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总之，在 MovingMNIST 上训练 ConvLSTM 模型的最佳轮数可能会有所不同并取决于许多因素，找到最佳轮数需...
MovingMNIST(is_train=True, root='data/', n_frames_input=args.frames_input, n_frames_output=args.frames_output, num_objects=[3]) is_train: If True, use script to generate data. If False, directly use Moving Mnist data downloaded fromhttp://www.cs.toronto.edu/~nitish/unsupervised_video...
The Moving MNIST dataset contains 10,000 video sequences, each consisting of 20 frames. In each video sequence, two digits move independently around the frame, which has a spatial resolution of 64×64 pixels. The digits frequently intersect with each oth
convLSTM_movingMNIST 使用convLSTM单元从移动的MNIST数据集中预测帧。 有两种模型: 单步模型 编码器结构。 接收19个移动的MNIST帧和attemts的序列以输出第20帧 多步模型 编码器-解码器结构。 接收10到19个移动MNIST帧(每个批次随机选择的序列长度)的序列,并尝试输出其余帧。 在训练过程中,使用单步模型的权重初始化...
The current state-of-the-art on Moving MNIST is PredFormer. See a full comparison of 30 papers with code.
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