Now, we will present how this kind of model might be implemented in TensorFlow. It is extremely similar to a model for classification, except that we have multiple output layers at the end instead of just one, and each has its own loss function:

def build_graph(self): 
   self.__x_ = tf.placeholder("float", shape=[None, 240, 320, 3], name='X') 
   self.__y_box = tf.placeholder("float", shape=[None, 4], name='Y_box') 
   self.__y_obj = tf.placeholder("float", shape=[None, 1], name='Y_obj') 
   # Training flag for dropout in the fully connected layers 
   self.__is_training = tf.placeholder(tf.bool) 
 
   with tf.name_scope("model") as scope: 
       conv1 = tf.layers.conv2d(inputs=self.__x_, filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu) 
       pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) 
       conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5], padding="same", activation=tf.nn.relu) 
       pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) 
       conv3 = tf.layers.conv2d(inputs=pool2, filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu) 
       pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2) 
       pool3_flat = tf.reshape(pool3, [-1, 40 * 30 * 32]) 
 
       # 2 Head version (has object head, and bounding box) 
       self.__model_box = tf.layers.dense(inputs=pool3_flat, units=4) 
       self.__model_has_obj = tf.layers.dense(inputs=pool3_flat, units=1, activation=tf.nn.sigmoid) 
 
   with tf.name_scope("loss_func") as scope: 
       loss_obj = tf.losses.log_loss(labels=self.__y_obj, predictions=self.__model_has_obj) 
       loss_bbox = tf.losses.huber_loss(labels=self.__y_box, predictions=self.__model_box) 
       # Get ratio of samples with objects 
       batch_size = tf.cast(tf.shape(self.__y_obj)[0], tf.float32) 
       num_objects_label = tf.cast(tf.count_nonzero(tf.cast(self.__y_obj > 0.0, tf.float32)), tf.float32) 
       ratio_has_objects = (num_objects_label * tf.constant(100.0)) / batch_size 
       # Loss function that has an "ignore" factor on the bbox loss when objects is not detected 
       self.__loss = loss_obj + (loss_bbox*ratio_has_objects) 
       # Add loss to tensorboard 
       tf.summary.scalar("loss", self.__loss) 
       tf.summary.scalar("loss_bbox", loss_bbox) 
       tf.summary.scalar("loss_obj", loss_obj) 
 
   with tf.name_scope("optimizer") as scope: 
       self.__train_step = tf.train.AdamOptimizer(1e-4).minimize(self.__loss) 
 
   # Merge op for tensorboard 
   self.__merged_summary_op = tf.summary.merge_all() 
 
   # Build graph 
   init = tf.global_variables_initializer() 
 
   # Saver for checkpoints 
   self.__saver = tf.train.Saver(max_to_keep=None) 
 
   # Avoid allocating the whole memory 
   gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6) 
   self.__session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) 
   # Configure summary to output at given directory 
   self.__writer = tf.summary.FileWriter("./logs/loc_logs", self.__session.graph) 
   self.__session.run(init)