A Step-by-Step Guide to Implementing RetinaNet for Object Detection using Keras and Detectron2

Introduction :

As we discussed in the last article, RetinaNet is a state-of-the-art object detection algorithm that combines the two-stage object detection framework with a single-shot detection architecture. It is a combination of two major ideas, anchor-based detection, and focal loss, to improve object detection performance. RetinaNet uses anchor boxes to generate object proposals, similar to the two-stage detection frameworks, but it predicts the object categories and locations using a single network, making the inference process more efficient. Additionally, RetinaNet introduces the novel focal loss function to address the issue of class imbalance in object detection. With the combination of anchor-based detection and focal loss, RetinaNet has demonstrated significant improvement in object detection accuracy on various benchmarks.

In this article, we're going to see how to implement RetinaNet for object detection. RetinaNet can be implemented using the Keras API and Detectron2, which is a high-level library for building and training deep-learning models in Python. Here are the steps to implement RetinaNet using Keras:

• Prepare the data: Load the images and labels, resize the images to a fixed size, and split the data into training and validation sets.
• Define the model: Use the functional API in Keras to define the RetinaNet architecture, including the feature extractor network, the classification sub-network, and the regression sub-network. You can use pre-trained models, such as ResNet or FPN, as the feature extractor network, and add your own layers for the detection head.
• Compile the model: Compile the model by specifying the optimizer, loss function, and metrics that you want to track. You can use the balanced focal loss function as the loss function.
• Train the model: Train the model on the training set, and validate it on the validation set. You can use the fit() method in Keras to start the training process.
• Evaluate the model: Evaluate the model on the test set, and calculate the performance metrics, such as mean average precision (mAP) or average precision (AP).
• Save and load the model: Save the model to disk using the save() method in Keras, and load the model from disk using the load_model() method.

Here's an example code snippet to get you started with implementing RetinaNet in Keras:

import numpy as np
import keras
from keras.applications import ResNet50
from keras.layers import Input, Dense, Conv2D, Flatten, Concatenate
from keras.models import Model

# Define the feature extractor network using ResNet50
input_tensor = Input(shape=(224,224,3))
feature_extractor = ResNet50(include_top=False, weights='imagenet', input_tensor=input_tensor)


# Define the classification sub-network
classification = Conv2D(filters=9, kernel_size=(3,3), activation='relu')(feature_extractor.output)
classification = Flatten()(classification)
classification = Dense(units=9, activation='sigmoid')(classification)


# Define the regression sub-network
regression = Conv2D(filters=36, kernel_size=(3,3), activation='relu')(feature_extractor.output)
regression = Flatten()(regression)
regression = Dense(units=4)(regression)


# Concatenate the outputs from the classification and regression sub-networks
output = Concatenate()([classification, regression])


# Define the final model
model = Model(inputs=input_tensor, outputs=output)


# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])


# Train the model on the training set
model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_val, y_val))


# Evaluate the model on the test set
test_loss, test_acc = model.evaluate(x_test, y_test)        

Get the complete code from here.

Also, we can use Detectron2 to train RetinaNet. Training RetinaNet using Detectron2 requires the following steps:

• Prepare the dataset: This involves preparing the annotated images and corresponding labels that the model will be trained on.
• Install Detectron2: To train RetinaNet using Detectron2, you first need to install the library.
• Configure RetinaNet in Detectron2: You need to create a configuration file specifying the parameters for training, such as batch size, learning rate, and the number of training epochs.
• Start the training process: You can then start the training process using the Detectron2 library. You can monitor the progress of the training using TensorBoard.
• Evaluate the model: After training, you can evaluate the performance of the model using metrics such as mean average precision (mAP) and evaluate its performance on the validation set.

import detectron2
from detectron2.engine import DefaultTrainer
from detectron2.config import get_cfg


cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/retinanet_R_101_FPN_3x.yaml"))
cfg.DATASETS.TRAIN = ("my_dataset_train",)
cfg.DATASETS.TEST = ("my_dataset_val",)
cfg.DATALOADER.NUM_WORKERS = 2
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml")  # Let training initialize from model zoo
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.00025? # pick a good LR
cfg.SOLVER.MAX_ITER = 300? ? # 300 iterations seems good enough for this toy dataset; you may need to train longer for a practical dataset
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128? ?
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2 # your number of classes (Number of foreground classes) 


trainer = DefaultTrainer(cfg)?
trainer.resume_or_load(resume=False)
trainer.train()

        

Get the complete code from here.