Added Additional Notes to README

README.md

@@ -52,6 +52,9 @@ Extracting the intermediate features for a given layer in the PredNet can be don
 ### Multi-Step Prediction
 The PredNet argument ```extrap_start_time``` can be used to force multi-step prediction. Starting at this time step, the prediction from the previous time step will be treated as the actual input. For example, if the model is run on a sequence of 15 timesteps with ```extrap_start_time = 10```, the last output will correspond to a t+5 prediction. In the paper, we train in this setting starting from the original t+1 trained weights (see `kitti_extrap_finetune.py`), and the resulting fine-tuned weights are included in `download_models.sh`.
 
+### Additional Notes
+When training on a new dataset, the image size has to be divisible by 2^(nb of layers - 1) because of the cyclical 2x2 max-pooling and upsampling operations.
+
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 <sup>1</sup> Note on implementation:  PredNet inherits from the Recurrent layer class, i.e. it has an internal state and a step function. Given the top-down then bottom-up update sequence, it must currently be implemented in Keras as essentially a 'super' layer where all layers in the PredNet are in one PredNet 'layer'. This is less than ideal, but it seems like the most efficient way as of now. We welcome suggestions if anyone thinks of a better implementation.