Code for feedforward and recurrent neural network models used in this preprint.
The code has been tested with TensorFlow 1.13 and Python 3.6.
The following code snippet shows how to build the Keras model and generate a prediction for a random image. A full example of extracting activations from a pre-trained model is given here.
import numpy as np
import tensorflow as tf
tf.enable_v2_behavior()
from rcnn_sat import preprocess_image, bl_net
# create the model with randomly initialised weights
input_layer = tf.keras.layers.Input((128, 128, 3))
model = bl_net(input_layer, classes=565, cumulative_readout=True)
# predict a random image
img = np.random.randint(0, 256, [1, 128, 128, 3], dtype=np.uint8)
model.predict(preprocess_image(img)) # softmax for each time step
The checkpoint files for pre-trained eco-set and imagenet models are hosted here.
Notes on pre-trained models:
- Pre-trained models expect 128 x 128 images as input.
- ImageNet models have
classes=1000
and ecoset models haveclasses=565
- BL models were trained with
cumulative_readout=False
but can be tested using either option - Model predictions correspond to the order of the categories within the files in
pretrained_output_categories
.