Day # 147 - Part II - Deep Learning techniques for Computer Vision applied to embedded systems

A very interesting Final Year Paper - Deep Learning techniques for Computer Vision applied to embedded systems

Part Two Series.

	import tensorflow as tf
	from tensorflow import keras
	model = keras.Sequential()
	model.add(keras.layers.Dense(64,activation='relu'))
	model.add(keras.layers.Dense(64,activation='relu'))
	model.add(keras.layers.Dense(10,activation='softmax'))
	model.compile(optimizer=tf.train.AdamOptimizer(0.001),loss='categorical_crossentropy',metrics=['accuracy'])
	import numpy as np
	data = np.random.random((1000,32))
	labels = np.random.random((1000,10))
	model.fit(data,labels,epochs=10,batch_size=32)

view raw keras1.py hosted with ❤ by GitHub

	import tensorflow as tf
	#load mnist dataset
	mnist = tf.keras.datasets.mnist
	#split into train and test
	(x_train,y_train),(x_test,y_test)=mnist.load_data()
	x_train,x_test=x_train/255.0,x_test/255.0
	#composing the model
	model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
	tf.keras.layers.Dense(512,activation=tf.nn.relu),
	tf.keras.layers.Dropout(0.2),
	tf.keras.layers.Dense(10,activation=tf.nn.softmax)])
	#configuring training parameters
	model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
	#start training
	model.fit(x_train,y_train,epochs=5)
	#evaluating the model
	model.evaluate(x_test,y_test)

view raw keras2.py hosted with ❤ by GitHub

Creating a custom Object Detector Machines

Steps Involved

• Dataset Preperation (Download images using - Fatkun Batch Download Images)
• Label Images by Hand (Painful process) - RectLabel Tool for manually labelling
• Convert into .tfrecords - Custom Tensorflow code to prepare .tfrecords
• Create labels with .pbtxt format
• Create bounding boxes
• Set TF Object Detection API
• Create Pipeline for Training - Configure model, train_config, train_input_header, eval_config, eval_input_reader
• Perform Training
• Monitor Performance
• Export Graph
• Compile for Vision Bonnet
• Deploy and Test

This is the first and most exhaustive step-by-step documentation neatly mentioned.

Happy Learning!!!