Object detection using YOLO

1. In YOLOv5, what does the .yaml configuration file define in terms of model training?

• Only the learning rate and optimizer settings

• Network architecture and dataset structure

• Input image resolution and loss function

• Anchor boxes and Intersection-over-Union (IoU) thresholds

2. What is the primary purpose of the 'hyp.yaml' file in YOLOv5?

• To set the evaluation metrics

• To configure the model's architecture

• To specify the training dataset paths

• To define the model's hyperparameters

3. In YOLOv5, which metric is commonly used to evaluate the model's performance?

• Mean Average Precision (mAP)

• Root Mean Square Error (RMSE)

• Mean Absolute Error (MAE)

• F1 Score

4. What role do anchor boxes play in YOLOv5?

• They define the input image size

• They help in bounding box prediction

• They determine the learning rate

• They are used for data augmentation

When running YOLOv5 in Python, what does the confidence score represent for a given detection?

• IoU between ground truth and predicted box

• Probability of object presence × class probability

• Classification accuracy on validation set

• Threshold used during training only

5. In YOLOv5, what is the significance of the 'weights' file during inference?

• It contains the model's architecture

• It specifies the output layer configurations

• It stores the trained model parameters

• It defines the input preprocessing steps

6. How does YOLOv5 handle multi-scale detection?

• By predicting at multiple layers of the network

• By resizing images to a fixed size

• By applying feature pyramid networks

• By using different models for each scale

7. What is the purpose of the 'data.yaml' file in YOLOv5?

• To set the evaluation metrics

• To configure the hyperparameters

• To specify the training and validation datasets

• To define the model's architecture

Which of the following Python libraries is essential for interpreting YOLO .pt weights in a custom script?

• TensorFlow

• OpenCV

• PyTorch

• Keras

What is the function of the 'hyp.scratch.yaml' file in YOLOv5?

• To initialize the model with random weights

• To define the training hyperparameters for a new model

• To specify the dataset augmentation techniques

• To configure the model's output layer