On Convolutional Vision Transformers for Yield Prediction
While a variety of methods offer good yield prediction on histogrammed remote sensing data, vision Transformers are only sparsely represented in the literature. The Convolution vision Transformer (CvT) is being tested to evaluate vision Transformers that are currently achieving state-of-the-art results in many other vision tasks. CvT combines some of the advantages of convolution with the advantages of dynamic attention and global context fusion of Transformers. It performs worse than widely tested methods such as XGBoost and CNNs, but shows that Transformers have potential to improve yield prediction.
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Methods
Absolute Position Encodings •
Adam •
Average Pooling •
Batch Normalization •
BPE •
Convolution •
CvT •
Dense Connections •
Depthwise Convolution •
Depthwise Separable Convolution •
Dropout •
Label Smoothing •
Layer Normalization •
Linear Layer •
Multi-Head Attention •
Pointwise Convolution •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Transformer •
Vision Transformer
