import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tf_explain.core.activations import ExtractActivations
from tensorflow.keras.applications.xception import decode_predictions
%matplotlib inline
load Xception pre-trained CNN model
model = tf.keras.applications.xception.Xception(weights='imagenet',
include_top=True)
I want to do it for tiny-yolov3, to understand key features for an image detection.
Has anyone done this ?
Hi, I am looking to visualize the activations layers for a given image for tiny yolov3.
Example : https://medium.com/google-developer-experts/interpreting-deep-learning-models-for-computer-vision-f95683e23c1d
Toward the end, they have done for another model,
load dependencies
import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from tf_explain.core.activations import ExtractActivations from tensorflow.keras.applications.xception import decode_predictions
%matplotlib inline
load Xception pre-trained CNN model
model = tf.keras.applications.xception.Xception(weights='imagenet', include_top=True) I want to do it for tiny-yolov3, to understand key features for an image detection. Has anyone done this ?