Deep Learning 6-Object Detection with Retinanet

by SN Gurukul on May 10, 2020 in Deep Learning in 15days

Learn A-Z Deep Learning in 15 Days

In this Lecture, will learn about Objection Detection with Retinanet

Retinanet

The one-stage RetinaNet network architecture uses a Feature Pyramid Network (FPN) backbone on top of a feedforward ResNet architecture (a) to generate a rich, multi-scale convolutional feature pyramid (b). To this backbone, RetinaNet attaches two subnetworks, one for classifying anchor boxes (c) and one for regressing from anchor boxes to ground-truth object boxes (d). The network design is intentionally simple, which enables this work to focus on a novel focal loss function that eliminates the accuracy gap between our one-stage detector and state-of-the-art two-stage detectors like Faster R-CNN with FPN while running at faster speeds.

Data Preprocessing with Berekely dataset

Categories frequency

car-----------------------713211

traffic sign---------------239686

traffic light-------------186117

person--------------------91349

truck-----------------------29971

bus-----------------------11672

bike-----------------------7210

rider-----------------------4517

motor-----------------------3002

train-----------------------136

Total annotation count = 1286871
Total images = 69863

**Run the Code in Google Colab

import os
import pandas as pd
import json

df = pd.DataFrame()
filepath='./bdd100k/labels/bdd100k_labels_images_train.json'
jsfile = json.loads(open(filepath).read())

for js in jsfile:
for label_point in js['labels']:
if label_point['category']=='bike' :

df=df.append({'label':label_point['category'],
'xmin': str(label_point['box2d']['x1']), 'ymin': str(label_point['box2d']['y1']),
'xmax': str(label_point['box2d']['x2']), 'ymax':str(label_point['box2d']['y2']),
'name':js['name']}, ignore_index=True)

df.to_csv('./bdd100k_train.csv', index=False, sep=",", header= False)

Create Mapping for class

class name to ID mapping file should contain one mapping per line

Retinanet Implementation

!git clone https://github.com/fizyr/keras-retinanet.git

cd /content/keras-retinanet/

!python setup.py build_ext --inplace

!pip install keras_retinanet

Train Retinanet

Upload the 'bdd100k_train.csv' file in the 'keras-retinanet'
Upload the 'mapping_file.csv' file in the 'fkeras-retinanet'

!python keras_retinanet/bin/train.py --batch-size 1 --epochs 50 --tensorboard-dir ./Tensorboard_files/ --steps 171487 csv bdd100k_train.csv mapping_file.csv

Convert the 'resnet50' to 'inference' model

!python keras_retinanet/bin/convert_model.py snapshots/resnet50_csv_02.h5 inference/inference_v20.h5

Test Retinanet model