안녕하세요, HELLO
오늘은 DeepLearning.AI에서 진행하는 앤드류 응(Andrew Ng) 교수님의 딥러닝 전문화의 네 번째 과정인 "Convolutional Neural Networks"을 정리하려고 합니다.
"Convolutional Neural Networks"의 강의를 통해 '자율 주행, 얼굴 인식, 방사선 이미지 인식' 등을 이해하고, CNN 모델에 대해서 배우게 됩니다. 강의는 아래와 같이 구성되어 있습니다.
~ Foundations of Convolutional Neural Networks
~ Deep Convolutional Models: Case Studies
~ Object Detection
~ Special Applications: Face recognition & Neural Style Transfer
"Convolutional Neural Networks" (Andrew Ng)의 3주차 "Autonomous driving application Car detection"의 강의 내용입니다.
CHAPTER 0. 'Packages'
CHAPTER 1. 'Problem Statement'
CHAPTER 2. 'YOLO'
CHAPTER 3. 'Test YOLO Pre-trained Model on Images'
CHAPTER 4. 'Summary for YOLO'
CHAPTER 0. 'Packages'
import argparse
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
import scipy.io
import scipy.misc
import numpy as np
import pandas as pd
import PIL
from PIL import ImageFont, ImageDraw, Image
import tensorflow as tf
from tensorflow.python.framework.ops import EagerTensor
from tensorflow.keras.models import load_model
from yad2k.models.keras_yolo import yolo_head
from yad2k.utils.utils import draw_boxes, get_colors_for_classes, scale_boxes, read_classes, read_anchors, preprocess_image
%matplotlib inlineCHAPTER 1. 'Problem Statement'
You are working on a self-driving car. Go you! As a critical component of this project, you'd like to first build a car detection system. To collect data, you've mounted a camera to the hood (meaning the front) of the car, which takes pictures of the road ahead every few seconds as you drive around. You've gathered all these images into a folder and labelled them by drawing bounding boxes around every car you found. Here's an example of what your bounding boxes look like:
If there are 80 classes you want the object detector to recognize, you can represent the class label 𝑐c either as an integer from 1 to 80, or as an 80-dimensional vector (with 80 numbers) one component of which is 1, and the rest of which are 0. The video lectures used the latter representation; in this notebook, you'll use both representations, depending on which is more convenient for a particular step.
In this exercise, you'll discover how YOLO ("You Only Look Once") performs object detection, and then apply it to car detection. Because the YOLO model is very computationally expensive to train, the pre-trained weights are already loaded for you to use.
CHAPTER 2. 'YOLO'
"You Only Look Once" (YOLO) is a popular algorithm because it achieves high accuracy while also being able to run in real time. This algorithm "only looks once" at the image in the sense that it requires only one forward propagation pass through the network to make predictions. After non-max suppression, it then outputs recognized objects together with the bounding boxes.
□ Model Details
Inputs and outputs
- The input is a batch of images, and each image has the shape (m, 608, 608, 3)
- The output is a list of bounding boxes along with the recognized classes. Each bounding box is represented by 6 numbers (𝑝𝑐,𝑏𝑥,𝑏𝑦,𝑏ℎ,𝑏𝑤,𝑐) as explained above. If you expand 𝑐 into an 80-dimensional vector, each bounding box is then represented by 85 numbers.
Anchor Boxes
- Anchor boxes are chosen by exploring the training data to choose reasonable height/width ratios that represent the different classes. For this assignment, 5 anchor boxes were chosen for you (to cover the 80 classes), and stored in the file './model_data/yolo_anchors.txt'
- The dimension for anchor boxes is the second to last dimension in the encoding: (𝑚,𝑛𝐻,𝑛𝑊,𝑎𝑛𝑐ℎ𝑜𝑟𝑠,𝑐𝑙𝑎𝑠𝑠𝑒𝑠).
- The YOLO architecture is: IMAGE (m, 608, 608, 3) -> DEEP CNN -> ENCODING (m, 19, 19, 5, 85).
Encoding
Let's look in greater detail at what this encoding represents.
If the center/midpoint of an object falls into a grid cell, that grid cell is responsible for detecting that object.
Since you're using 5 anchor boxes, each of the 19 x19 cells thus encodes information about 5 boxes. Anchor boxes are defined only by their width and height. For simplicity, you'll flatten the last two dimensions of the shape (19, 19, 5, 85) encoding, so the output of the Deep CNN is (19, 19, 425).
□ Class score
Now, for each box (of each cell) you'll compute the following element-wise product and extract a probability that the box contains a certain class. The class score is 𝑠𝑐𝑜𝑟𝑒𝑐,𝑖=𝑝𝑐×𝑐𝑖: the probability that there is an object 𝑝𝑐 times the probability that the object is a certain class 𝑐𝑖.
□ Visualizing classes
Here's one way to visualize what YOLO is predicting on an image:
- For each of the 19x19 grid cells, find the maximum of the probability scores (taking a max across the 80 classes, one maximum for each of the 5 anchor boxes).
- Color that grid cell according to what object that grid cell considers the most likely.
Doing this results in this picture:
□ Visualizing bounding boxes
Another way to visualize YOLO's output is to plot the bounding boxes that it outputs. Doing that results in a visualization like this:
□ Non-Max suppression
In the figure above, the only boxes plotted are ones for which the model had assigned a high probability, but this is still too many boxes. You'd like to reduce the algorithm's output to a much smaller number of detected objects.
To do so, you'll use non-max suppression. Specifically, you'll carry out these steps:
- Get rid of boxes with a low score. Meaning, the box is not very confident about detecting a class, either due to the low probability of any object, or low probability of this particular class.
- Select only one box when several boxes overlap with each other and detect the same object.
□ Filtering with a Threshold on Class Scores
You're going to first apply a filter by thresholding, meaning you'll get rid of any box for which the class "score" is less than a chosen threshold.
The model gives you a total of 19x19x5x85 numbers, with each box described by 85 numbers. It's convenient to rearrange the (19,19,5,85) (or (19,19,425)) dimensional tensor into the following variables:
- box_confidence: tensor of shape (19,19,5,1) containing 𝑝𝑐 (confidence probability that there's some object) for each of the 5 boxes predicted in each of the 19x19 cells.
- boxes: tensor of shape (19,19,5,4) containing the midpoint and dimensions (𝑏𝑥,𝑏𝑦,𝑏ℎ,𝑏𝑤) for each of the 5 boxes in each cell.
- box_class_probs: tensor of shape (19,19,5,80) containing the "class probabilities" (𝑐1,𝑐2,...𝑐80) for each of the 80 classes for each of the 5 boxes per cell.
# UNQ_C1 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: yolo_filter_boxes
def yolo_filter_boxes(boxes, box_confidence, box_class_probs, threshold = .6):
"""Filters YOLO boxes by thresholding on object and class confidence.
Arguments:
boxes -- tensor of shape (19, 19, 5, 4)
box_confidence -- tensor of shape (19, 19, 5, 1)
box_class_probs -- tensor of shape (19, 19, 5, 80)
threshold -- real value, if [ highest class probability score < threshold],
then get rid of the corresponding box
Returns:
scores -- tensor of shape (None,), containing the class probability score for selected boxes
boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes
classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes
Note: "None" is here because you don't know the exact number of selected boxes, as it depends on the threshold.
For example, the actual output size of scores would be (10,) if there are 10 boxes.
"""
### START CODE HERE
# Step 1: Compute box scores
##(≈ 1 line)
box_scores = box_confidence * box_class_probs # TensorShape([19, 19, 5, 80])
# Step 2: Find the box_classes using the max box_scores, keep track of the corresponding score
##(≈ 2 lines)
box_classes = tf.math.argmax(box_scores, axis=-1)
box_class_scores = tf.math.reduce_max(box_scores, axis=-1)
# Step 3: Create a filtering mask based on "box_class_scores" by using "threshold". The mask should have the
# same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)
## (≈ 1 line)
filtering_mask = box_class_scores >= threshold
# Step 4: Apply the mask to box_class_scores, boxes and box_classes
## (≈ 3 lines)
scores = tf.boolean_mask(box_class_scores, filtering_mask)
boxes = tf.boolean_mask(boxes, filtering_mask)
classes = tf.boolean_mask(box_classes, filtering_mask)
### END CODE HERE
return scores, boxes, classes
# BEGIN UNIT TEST
tf.random.set_seed(10)
box_confidence = tf.random.normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1)
boxes = tf.random.normal([19, 19, 5, 4], mean=1, stddev=4, seed = 1)
box_class_probs = tf.random.normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1)
scores, boxes, classes = yolo_filter_boxes(boxes, box_confidence, box_class_probs, threshold = 0.5)
print("scores[2] = " + str(scores[2].numpy()))
print("boxes[2] = " + str(boxes[2].numpy()))
print("classes[2] = " + str(classes[2].numpy()))
print("scores.shape = " + str(scores.shape))
print("boxes.shape = " + str(boxes.shape))
print("classes.shape = " + str(classes.shape))
assert type(scores) == EagerTensor, "Use tensorflow functions"
assert type(boxes) == EagerTensor, "Use tensorflow functions"
assert type(classes) == EagerTensor, "Use tensorflow functions"
assert scores.shape == (1789,), "Wrong shape in scores"
assert boxes.shape == (1789, 4), "Wrong shape in boxes"
assert classes.shape == (1789,), "Wrong shape in classes"
assert np.isclose(scores[2].numpy(), 9.270486), "Values are wrong on scores"
assert np.allclose(boxes[2].numpy(), [4.6399336, 3.2303846, 4.431282, -2.202031]), "Values are wrong on boxes"
assert classes[2].numpy() == 8, "Values are wrong on classes"
print("\033[92m All tests passed!")
# END UNIT TEST
□ Non-max Suppression
Even after filtering by thresholding over the class scores, you still end up with a lot of overlapping boxes. A second filter for selecting the right boxes is called non-maximum suppression (NMS).
Non-max suppression uses the very important function called "Intersection over Union", or IoU.
# UNQ_C2 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: iou
def iou(box1, box2):
"""Implement the intersection over union (IoU) between box1 and box2
Arguments:
box1 -- first box, list object with coordinates (box1_x1, box1_y1, box1_x2, box_1_y2)
box2 -- second box, list object with coordinates (box2_x1, box2_y1, box2_x2, box2_y2)
"""
(box1_x1, box1_y1, box1_x2, box1_y2) = box1
(box2_x1, box2_y1, box2_x2, box2_y2) = box2
### START CODE HERE
# Calculate the (yi1, xi1, yi2, xi2) coordinates of the intersection of box1 and box2. Calculate its Area.
##(≈ 7 lines)
xi1 = max(box1_x1, box2_x1)
yi1 = max(box1_y1, box2_y1)
xi2 = min(box1_x2, box2_x2)
yi2 = min(box1_y2, box2_y2)
inter_width = xi2 - xi1
inter_height = yi2 - yi1
inter_area = max(inter_height, 0) * max(inter_width ,0) # it could be negative parts
# Calculate the Union area by using Formula: Union(A,B) = A + B - Inter(A,B)
## (≈ 3 lines)
box1_area = (box1_x2 - box1_x1) * (box1_y2 - box1_y1)
box2_area = (box2_x2 - box2_x1) * (box2_y2 - box2_y1)
union_area = box2_area + box1_area - inter_area
# compute the IoU
iou = inter_area / union_area
### END CODE HERE
return iou
# BEGIN UNIT TEST
## Test case 1: boxes intersect
box1 = (2, 1, 4, 3)
box2 = (1, 2, 3, 4)
print("iou for intersecting boxes = " + str(iou(box1, box2)))
assert iou(box1, box2) < 1, "The intersection area must be always smaller or equal than the union area."
assert np.isclose(iou(box1, box2), 0.14285714), "Wrong value. Check your implementation. Problem with intersecting boxes"
## Test case 2: boxes do not intersect
box1 = (1,2,3,4)
box2 = (5,6,7,8)
print("iou for non-intersecting boxes = " + str(iou(box1,box2)))
assert iou(box1, box2) == 0, "Intersection must be 0"
## Test case 3: boxes intersect at vertices only
box1 = (1,1,2,2)
box2 = (2,2,3,3)
print("iou for boxes that only touch at vertices = " + str(iou(box1,box2)))
assert iou(box1, box2) == 0, "Intersection at vertices must be 0"
## Test case 4: boxes intersect at edge only
box1 = (1,1,3,3)
box2 = (2,3,3,4)
print("iou for boxes that only touch at edges = " + str(iou(box1,box2)))
assert iou(box1, box2) == 0, "Intersection at edges must be 0"
print("\033[92m All tests passed!")
# END UNIT TEST
□ YOLO Non-max Suppression
You are now ready to implement non-max suppression. The key steps are:
- Select the box that has the highest score.
- Compute the overlap of this box with all other boxes, and remove boxes that overlap significantly (iou >= iou_threshold).
- Go back to step 1 and iterate until there are no more boxes with a lower score than the currently selected box.
This will remove all boxes that have a large overlap with the selected boxes. Only the "best" boxes remain.
# UNQ_C3 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: yolo_non_max_suppression
def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5):
"""
Applies Non-max suppression (NMS) to set of boxes
Arguments:
scores -- tensor of shape (None,), output of yolo_filter_boxes()
boxes -- tensor of shape (None, 4), output of yolo_filter_boxes() that have been scaled to the image size (see later)
classes -- tensor of shape (None,), output of yolo_filter_boxes()
max_boxes -- integer, maximum number of predicted boxes you'd like
iou_threshold -- real value, "intersection over union" threshold used for NMS filtering
Returns:
scores -- tensor of shape (, None), predicted score for each box
boxes -- tensor of shape (4, None), predicted box coordinates
classes -- tensor of shape (, None), predicted class for each box
Note: The "None" dimension of the output tensors has obviously to be less than max_boxes. Note also that this
function will transpose the shapes of scores, boxes, classes. This is made for convenience.
"""
max_boxes_tensor = tf.Variable(max_boxes, dtype='int32') # tensor to be used in tf.image.non_max_suppression()
### START CODE HERE
# Use tf.image.non_max_suppression() to get the list of indices corresponding to boxes you keep
##(≈ 1 line)
nms_indices = tf.image.non_max_suppression(boxes, scores, max_boxes, iou_threshold)
# Use tf.gather() to select only nms_indices from scores, boxes and classes
##(≈ 3 lines)
scores = tf.gather(scores, nms_indices)
boxes = tf.gather(boxes, nms_indices)
classes = tf.gather(classes, nms_indices)
### END CODE HERE
return scores, boxes, classes
# BEGIN UNIT TEST
tf.random.set_seed(10)
scores = tf.random.normal([54,], mean=1, stddev=4, seed = 1)
boxes = tf.random.normal([54, 4], mean=1, stddev=4, seed = 1)
classes = tf.random.normal([54,], mean=1, stddev=4, seed = 1)
scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes)
assert type(scores) == EagerTensor, "Use tensoflow functions"
print("scores[2] = " + str(scores[2].numpy()))
print("boxes[2] = " + str(boxes[2].numpy()))
print("classes[2] = " + str(classes[2].numpy()))
print("scores.shape = " + str(scores.numpy().shape))
print("boxes.shape = " + str(boxes.numpy().shape))
print("classes.shape = " + str(classes.numpy().shape))
assert type(scores) == EagerTensor, "Use tensoflow functions"
assert type(boxes) == EagerTensor, "Use tensoflow functions"
assert type(classes) == EagerTensor, "Use tensoflow functions"
assert scores.shape == (10,), "Wrong shape"
assert boxes.shape == (10, 4), "Wrong shape"
assert classes.shape == (10,), "Wrong shape"
assert np.isclose(scores[2].numpy(), 8.147684), "Wrong value on scores"
assert np.allclose(boxes[2].numpy(), [ 6.0797963, 3.743308, 1.3914018, -0.34089637]), "Wrong value on boxes"
assert np.isclose(classes[2].numpy(), 1.7079165), "Wrong value on classes"
print("\033[92m All tests passed!")
# END UNIT TEST
□ Wrapping Up the Filtering
It's time to implement a function taking the output of the deep CNN (the 19x19x5x85 dimensional encoding) and filtering through all the boxes using the functions you've just implemented.
def yolo_boxes_to_corners(box_xy, box_wh):
"""Convert YOLO box predictions to bounding box corners."""
box_mins = box_xy - (box_wh / 2.)
box_maxes = box_xy + (box_wh / 2.)
return tf.keras.backend.concatenate([
box_mins[..., 1:2], # y_min
box_mins[..., 0:1], # x_min
box_maxes[..., 1:2], # y_max
box_maxes[..., 0:1] # x_max
])
# UNQ_C4 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: yolo_eval
def yolo_eval(yolo_outputs, image_shape = (720, 1280), max_boxes=10, score_threshold=.6, iou_threshold=.5):
"""
Converts the output of YOLO encoding (a lot of boxes) to your predicted boxes along with their scores, box coordinates and classes.
Arguments:
yolo_outputs -- output of the encoding model (for image_shape of (608, 608, 3)), contains 4 tensors:
box_xy: tensor of shape (None, 19, 19, 5, 2)
box_wh: tensor of shape (None, 19, 19, 5, 2)
box_confidence: tensor of shape (None, 19, 19, 5, 1)
box_class_probs: tensor of shape (None, 19, 19, 5, 80)
image_shape -- tensor of shape (2,) containing the input shape, in this notebook we use (608., 608.) (has to be float32 dtype)
max_boxes -- integer, maximum number of predicted boxes you'd like
score_threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box
iou_threshold -- real value, "intersection over union" threshold used for NMS filtering
Returns:
scores -- tensor of shape (None, ), predicted score for each box
boxes -- tensor of shape (None, 4), predicted box coordinates
classes -- tensor of shape (None,), predicted class for each box
"""
### START CODE HERE
# Retrieve outputs of the YOLO model (≈1 line)
box_xy, box_wh, box_confidence, box_class_probs = yolo_outputs
# Convert boxes to be ready for filtering functions (convert boxes box_xy and box_wh to corner coordinates)
boxes = yolo_boxes_to_corners(box_xy, box_wh)
# Use one of the functions you've implemented to perform Score-filtering with a threshold of score_threshold (≈1 line)
scores, boxes, classes = yolo_filter_boxes(boxes, box_confidence, box_class_probs, threshold=score_threshold)
# Scale boxes back to original image shape.
boxes = scale_boxes(boxes, image_shape)
# Use one of the functions you've implemented to perform Non-max suppression with
# maximum number of boxes set to max_boxes and a threshold of iou_threshold (≈1 line)
scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes, iou_threshold)
### END CODE HERE
return scores, boxes, classes
# BEGIN UNIT TEST
tf.random.set_seed(10)
yolo_outputs = (tf.random.normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),
tf.random.normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),
tf.random.normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1),
tf.random.normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1))
scores, boxes, classes = yolo_eval(yolo_outputs)
print("scores[2] = " + str(scores[2].numpy()))
print("boxes[2] = " + str(boxes[2].numpy()))
print("classes[2] = " + str(classes[2].numpy()))
print("scores.shape = " + str(scores.numpy().shape))
print("boxes.shape = " + str(boxes.numpy().shape))
print("classes.shape = " + str(classes.numpy().shape))
assert type(scores) == EagerTensor, "Use tensoflow functions"
assert type(boxes) == EagerTensor, "Use tensoflow functions"
assert type(classes) == EagerTensor, "Use tensoflow functions"
assert scores.shape == (10,), "Wrong shape"
assert boxes.shape == (10, 4), "Wrong shape"
assert classes.shape == (10,), "Wrong shape"
assert np.isclose(scores[2].numpy(), 171.60194), "Wrong value on scores"
assert np.allclose(boxes[2].numpy(), [-1240.3483, -3212.5881, -645.78, 2024.3052]), "Wrong value on boxes"
assert np.isclose(classes[2].numpy(), 16), "Wrong value on classes"
print("\033[92m All tests passed!")
# END UNIT TEST
CHAPTER 3. 'Test YOLO Pre-trained Model on Images'
□ Defining Classes, Anchors and Image Shape
You're trying to detect 80 classes, and are using 5 anchor boxes. The information on the 80 classes and 5 boxes is gathered in two files: "coco_classes.txt" and "yolo_anchors.txt". You'll read class names and anchors from text files. The car detection dataset has 720x1280 images, which are pre-processed into 608x608 images.
class_names = read_classes("model_data/coco_classes.txt")
anchors = read_anchors("model_data/yolo_anchors.txt")
model_image_size = (608, 608) # Same as yolo_model input layer size
□ Loading a Pre-trained Model
Training a YOLO model takes a very long time and requires a fairly large dataset of labelled bounding boxes for a large range of target classes. You are going to load an existing pre-trained Keras YOLO model stored in "yolo.h5". These weights come from the official YOLO website, and were converted using a function written by Allan Zelener. References are at the end of this notebook. Technically, these are the parameters from the "YOLOv2" model, but are simply referred to as "YOLO" in this notebook.
Run the cell below to load the model from this file.
yolo_model = load_model("model_data/", compile=False)□ Convert Output of the Model to Usable Bounding Box Tensors
The output of yolo_model is a (m, 19, 19, 5, 85) tensor that needs to pass through non-trivial processing and conversion. You will need to call yolo_head to format the encoding of the model you got from yolo_model into something decipherable:
yolo_model_outputs = yolo_model(image_data) yolo_outputs = yolo_head(yolo_model_outputs, anchors, len(class_names)) The variable yolo_outputs will be defined as a set of 4 tensors that you can then use as input by your yolo_eval function. If you are curious about how yolo_head is implemented, you can find the function definition in the file keras_yolo.py. The file is also located in your workspace in this path: yad2k/models/keras_yolo.py.
□ Filtering Boxes
yolo_outputs gave you all the predicted boxes of yolo_model in the correct format. To perform filtering and select only the best boxes, you will call yolo_eval, which you had previously implemented, to do so:
out_scores, out_boxes, out_classes = yolo_eval(yolo_outputs, [image.size[1], image.size[0]], 10, 0.3, 0.5)def predict(image_file):
"""
Runs the graph to predict boxes for "image_file". Prints and plots the predictions.
Arguments:
image_file -- name of an image stored in the "images" folder.
Returns:
out_scores -- tensor of shape (None, ), scores of the predicted boxes
out_boxes -- tensor of shape (None, 4), coordinates of the predicted boxes
out_classes -- tensor of shape (None, ), class index of the predicted boxes
Note: "None" actually represents the number of predicted boxes, it varies between 0 and max_boxes.
"""
# Preprocess your image
image, image_data = preprocess_image("images/" + image_file, model_image_size = (608, 608))
yolo_model_outputs = yolo_model(image_data)
yolo_outputs = yolo_head(yolo_model_outputs, anchors, len(class_names))
out_scores, out_boxes, out_classes = yolo_eval(yolo_outputs, [image.size[1], image.size[0]], 10, 0.3, 0.5)
# Print predictions info
print('Found {} boxes for {}'.format(len(out_boxes), "images/" + image_file))
# Generate colors for drawing bounding boxes.
colors = get_colors_for_classes(len(class_names))
# Draw bounding boxes on the image file
#draw_boxes2(image, out_scores, out_boxes, out_classes, class_names, colors, image_shape)
draw_boxes(image, out_boxes, out_classes, class_names, out_scores)
# Save the predicted bounding box on the image
image.save(os.path.join("out", image_file), quality=100)
# Display the results in the notebook
output_image = Image.open(os.path.join("out", image_file))
imshow(output_image)
return out_scores, out_boxes, out_classes
CHAPTER 4. 'Summary for YOLO'
- Input image (608, 608, 3)
- The input image goes through a CNN, resulting in a (19,19,5,85) dimensional output.
- After flattening the last two dimensions, the output is a volume of shape (19, 19, 425):
- Each cell in a 19x19 grid over the input image gives 425 numbers.
- 425 = 5 x 85 because each cell contains predictions for 5 boxes, corresponding to 5 anchor boxes, as seen in lecture.
- 85 = 5 + 80 where 5 is because (𝑝𝑐,𝑏𝑥,𝑏𝑦,𝑏ℎ,𝑏𝑤)(pc,bx,by,bh,bw) has 5 numbers, and 80 is the number of classes we'd like to detect
- You then select only few boxes based on:
- Score-thresholding: throw away boxes that have detected a class with a score less than the threshold
- Non-max suppression: Compute the Intersection over Union and avoid selecting overlapping boxes
- This gives you YOLO's final output.
What you should remember:
- YOLO is a state-of-the-art object detection model that is fast and accurate
- It runs an input image through a CNN, which outputs a 19x19x5x85 dimensional volume.
- The encoding can be seen as a grid where each of the 19x19 cells contains information about 5 boxes.
- You filter through all the boxes using non-max suppression. Specifically:
- Score thresholding on the probability of detecting a class to keep only accurate (high probability) boxes
- Intersection over Union (IoU) thresholding to eliminate overlapping boxes
- Because training a YOLO model from randomly initialized weights is non-trivial and requires a large dataset as well as lot of computation, previously trained model parameters were used in this exercise. If you wish, you can also try fine-tuning the YOLO model with your own dataset, though this would be a fairly non-trivial exercise.
■ 마무리
"Convolutional Neural Networks" (Andrew Ng)의3주차 "Autonomous driving application Car detection" 실습에 대해서 정리해봤습니다.
그럼 오늘 하루도 즐거운 나날 되길 기도하겠습니다
좋아요와 댓글 부탁드립니다 :)
감사합니다.
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