# Road_Object-Detection

Documentation

See the for full documentation on training, testing and deployment. See below for quickstart examples.

Install

Hi, if you want to see the video of the result of this project, click the link!

YouTube Link

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

>>> git clone https://github.com/AI-Expert-04/Road_Object_Detection.git  # clone
>>> conda create —name Road_Object-Detection-env python=3.8
>>> conda activate Road_Object-Detection-env
Pycharm Termainal >>> pip install -r requirements.txt # install

5. DBB100K data 다운 / DBB100K data Link / Video data Link

6. yolov3 and Ration Net Model 다운 / models Link

yolov 학습

model : MabileNet weight : imagenet optimizer : SGD

    model = tf.keras.applications.MobileNet(weights='imagenet', include_top=False,  input_shape=(224, 224, 3))
    model.trainable = False # 1000개의 가중치를 학습하지 않음.

    model = tf.keras.Sequential([
        model, # imageNet 전이 학습 Input_layer
        # Convolution Neural Network (Convolution 신경망)
        tf.keras.layers.Conv2D(1024, (3, 3), padding = 'SAME'), # padding 사용해 필터를 줄임
        tf.keras.layers.Conv2D(1024, (3, 3), padding='SAME'), # padding 한번 더해 필터를 더 줄임
        tf.keras.layers.GlobalAveragePooling2D(), # 필터에 사용될 Parameter 수를 줄여 차원을 감소 즉 2차원
        ## hidden_layer1 ~ hidden_layer2
        # 완전 연결 신경망
        tf.keras.layers.Dense(4096), # 1024 -> 4096(hidden_layer1)
        tf.keras.layers.Dense(735), # 4096(hidden_layer1) -> 735(hidden_layer2)
        tf.keras.layers.Reshape((7, 7, 15)) # 필터를 다시 되돌림. Output_layer
    ]); model.summary()

    if not os.path.exists('../logs'):
        os.mkdir('../logs')

    tensorboard = tf.keras.callbacks.TensorBoard(log_dir='../logs')

    # SGD(Stochastic Gradient Decent) 확률적 경사 하강법
    optimizer = tf.keras.optimizers.SGD(learning_rate=0.000001, momentum=0.9) # 최적화 함수
    model.compile(loss=yolo_multitask_loss, optimizer=optimizer, run_eagerly=True) # 실패 함수
    model.fit(images, labels, epochs=5, verbose=1, callbacks=[tensorboard]) # 학습
    if not os.path.exists('../models'):
        os.mkdir('../models')

    model.save('../models/yolo_trained.h5')

Retina 학습.

model.fit(
    train_dataset.take(10),
    validation_data=val_dataset.take(5),
    epochs=epochs,
    callbacks=callbacks_list,
    verbose=1,
)   

report Link