yolov5/tutorials/architecture_description/

[giscus[bot]]

yolov5/tutorials/architecture_description/

Explore the architecture of YOLOv5, an object detection algorithm by Ultralytics. Understand the model structure, data augmentation methods, training strategies, and loss computation techniques.

https://docs.ultralytics.com/yolov5/tutorials/architecture_description/

[labintsev]

Well explained, thanks a lot!

[glenn-jocher]

You're welcome! If you have any more questions or need further clarification on YOLOv8 or any other models, feel free to ask. Happy detecting! 😊🚀

[SudhanshuBlaze]

Hi, I want to use albumentation to augment my dataset and fine tune the yolov9e model.

Is albumentation in-built function in yolo if yes then how do I use it? Below is my code for fine-tuning yolo model:

from ultralytics import YOLO

yolo = YOLO('yolov9e.pt')
yolo.train(data='datasets/finetune_dataset/fine_tune_dataset.yml', epochs=50)
valid_results = yolo.val()
print(valid_results)

[pderrenger]

Hi there! Thanks for reaching out with your question about using Albumentations for dataset augmentation while fine-tuning the YOLOv9e model.

Currently, the Ultralytics YOLO framework does not have built-in support for Albumentations directly within the training pipeline. However, you can still integrate Albumentations into your data preprocessing workflow by customizing the data loading process.

Here's a step-by-step guide on how you can achieve this:

1. Install Albumentations:
   First, ensure you have Albumentations installed:

   pip install albumentations

2. Custom DataLoader:
   You'll need to create a custom DataLoader that applies Albumentations transformations to your dataset. Below is an example of how you can integrate Albumentations with PyTorch DataLoader:

   import albumentations as A
   from albumentations.pytorch import ToTensorV2
   from torch.utils.data import Dataset, DataLoader
   from ultralytics import YOLO
   import cv2
   import os
   
   class CustomDataset(Dataset):
       def __init__(self, annotations_file, img_dir, transform=None):
           self.img_labels = pd.read_csv(annotations_file)
           self.img_dir = img_dir
           self.transform = transform
   
       def __len__(self):
           return len(self.img_labels)
   
       def __getitem__(self, idx):
           img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
           image = cv2.imread(img_path)
           image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
           label = self.img_labels.iloc[idx, 1]
           if self.transform:
               augmented = self.transform(image=image)
               image = augmented['image']
           return image, label
   
   # Define Albumentations transformations
   transform = A.Compose([
       A.RandomCrop(width=256, height=256),
       A.HorizontalFlip(p=0.5),
       A.RandomBrightnessContrast(p=0.2),
       ToTensorV2()
   ])
   
   # Create dataset and dataloader
   dataset = CustomDataset(annotations_file='path/to/annotations.csv', img_dir='path/to/images', transform=transform)
   dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
   
   # Load YOLO model
   yolo = YOLO('yolov9e.pt')
   
   # Train the model
   yolo.train(data=dataloader, epochs=50)

3. Training:
   Use the custom DataLoader in your training loop. This example assumes you have a CSV file with image paths and labels. Adjust the dataset and DataLoader according to your specific dataset structure.

4. Validation:
   After training, you can validate your model as usual:

   valid_results = yolo.val()
   print(valid_results)

If you encounter any issues or have further questions, please ensure you are using the latest versions of torch and ultralytics. You can update them using:

pip install --upgrade torch ultralytics

For more detailed information on creating a minimum reproducible example, please refer to our Minimum Reproducible Example Guide.

Feel free to reach out if you need any more help. Happy coding! 😊

[SudhanshuBlaze]

Thanks for the prompt response, I will use this code and update it to also have testDataloader.

[pderrenger]

Hi there! 😊

Thank you for your prompt response and for planning to integrate testDataloader into your workflow. It's great to see your enthusiasm for fine-tuning the YOLOv9e model with Albumentations.

To clarify, while Albumentations is not directly built into the Ultralytics YOLO framework, you can certainly integrate it into your data preprocessing pipeline by customizing your DataLoader. This allows you to leverage the powerful augmentation techniques provided by Albumentations to enhance your dataset.

Here's a quick recap of how you can set up a custom DataLoader with Albumentations:

1. Install Albumentations:

   pip install albumentations

2. Create a Custom Dataset Class:
   Define a custom dataset class that applies Albumentations transformations.

   import albumentations as A
   from albumentations.pytorch import ToTensorV2
   from torch.utils.data import Dataset, DataLoader
   from ultralytics import YOLO
   import cv2
   import os
   import pandas as pd
   
   class CustomDataset(Dataset):
       def __init__(self, annotations_file, img_dir, transform=None):
           self.img_labels = pd.read_csv(annotations_file)
           self.img_dir = img_dir
           self.transform = transform
   
       def __len__(self):
           return len(self.img_labels)
   
       def __getitem__(self, idx):
           img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
           image = cv2.imread(img_path)
           image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
           label = self.img_labels.iloc[idx, 1]
           if self.transform:
               augmented = self.transform(image=image)
               image = augmented['image']
           return image, label
   
   # Define Albumentations transformations
   transform = A.Compose([
       A.RandomCrop(width=256, height=256),
       A.HorizontalFlip(p=0.5),
       A.RandomBrightnessContrast(p=0.2),
       ToTensorV2()
   ])
   
   # Create dataset and dataloader
   dataset = CustomDataset(annotations_file='path/to/annotations.csv', img_dir='path/to/images', transform=transform)
   dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

3. Train the YOLO Model:
   Use the custom DataLoader in your training loop.

   # Load YOLO model
   yolo = YOLO('yolov9e.pt')
   
   # Train the model
   yolo.train(data=dataloader, epochs=50)
   
   # Validate the model
   valid_results = yolo.val()
   print(valid_results)

If you encounter any issues or need further assistance, please ensure you are using the latest versions of torch and ultralytics. You can update them using:

pip install --upgrade torch ultralytics

For more detailed guidance on creating a minimum reproducible example, please refer to our Minimum Reproducible Example Guide.

Feel free to reach out if you have any more questions or need further assistance. Happy coding! 😊

[bnawras]

Hi! Let's consider the top left example in the image with examples of assigning the matched anchor to the appropriate cells.

My question is why the anchor is not assigned to the green cell, though it can be predicted for this cell since the offset is approximately ~1.2?

[pderrenger]

@bnawras hi there! Thank you for your insightful question and for providing the detailed images to illustrate your point.

In YOLOv5, the assignment of anchors to grid cells is a critical part of the detection process. The anchor is assigned to a grid cell based on the Intersection over Union (IoU) between the anchor and the ground truth box. The goal is to ensure that each ground truth box is matched with the most appropriate anchor, which helps in accurate object detection.

Regarding your specific question about why the anchor is not assigned to the green cell despite the offset being approximately ~1.2, it’s important to consider the following:

1. IoU Thresholds: YOLOv5 uses IoU thresholds to determine the best anchor for a given ground truth box. If the IoU between the anchor and the ground truth box in the green cell does not meet the required threshold, the anchor will not be assigned to that cell.

2. Anchor Matching Strategy: The model assigns anchors to grid cells based on the highest IoU. Even if the offset is close, the anchor might be better matched to another cell with a higher IoU.

3. Center Point Adjustment: YOLOv5 adjusts the center point offset range from (0, 1) to (-0.5, 1.5), allowing a ground truth box to be assigned to more than one anchor. This adjustment helps in reducing grid sensitivity and ensuring better anchor assignments.

Here’s a simplified example to illustrate anchor assignment:

import torch

# Example ground truth box (x_center, y_center, width, height)
gt_box = torch.tensor([0.5, 0.5, 0.4, 0.4])

# Example anchors (width, height)
anchors = torch.tensor([[0.3, 0.3], [0.5, 0.5], [0.7, 0.7]])

# Calculate IoU between ground truth box and anchors
def calculate_iou(box1, box2):
    inter = torch.min(box1[2:], box2[2:]) - torch.max(box1[:2], box2[:2])
    inter = torch.clamp(inter, min=0)
    inter_area = inter[0] * inter[1]
    box1_area = box1[2] * box1[3]
    box2_area = box2[2] * box2[3]
    union_area = box1_area + box2_area - inter_area
    return inter_area / union_area

ious = [calculate_iou(gt_box, torch.cat((torch.tensor([0.5, 0.5]), anchor))) for anchor in anchors]

# Assign anchor with highest IoU
best_anchor_idx = torch.argmax(torch.tensor(ious))
print(f"Best anchor index: {best_anchor_idx}, IoU: {ious[best_anchor_idx]}")

In this example, the anchor with the highest IoU is selected for the ground truth box.

For a more detailed explanation and further reading, I recommend checking out the YOLOv5 Architecture documentation.

If you have any further questions or need additional clarification, feel free to ask! 😊

[bnawras]

@pderrenger Thank you for the detailed reply, but your answer does not match the documentation

according to your documentation, there are 2 steps in building targets:

1. anchors assignment based on the heuristic $r^{max} < anchor_t$
   this step is clear to me; it doesn't depend on coordinates, only on the ratios of the anchors and the ground truth bbox dimensions
2. assignment of the matched anchors to appropriate cells based on center point offset

With this logic, the green cell should be assigned. Please correct me if I'm wrong.

[pderrenger]

Hi @bnawras,

Thank you for your detailed follow-up! I appreciate your keen observation and the effort to understand the anchor assignment process in YOLOv5.

You are correct that the anchor assignment involves two main steps:

1. Anchor Assignment Based on Heuristic: This step involves matching anchors to ground truth boxes based on the ratio of their dimensions. This step is independent of the coordinates and focuses solely on the size ratios.

2. Assignment of Matched Anchors to Appropriate Cells: This step involves assigning the matched anchors to grid cells based on the center point offset. The center point offset is crucial here, and it typically ranges from (0, 1) but is adjusted to (-0.5, 1.5) to reduce grid sensitivity and allow for more flexible assignments.

Regarding your specific query about the green cell, the assignment of anchors to cells is determined by the center point offset and the IoU threshold. Even if the offset is approximately ~1.2, the anchor might not be assigned to the green cell if the IoU between the anchor and the ground truth box does not meet the required threshold. The model aims to assign anchors to the cells that provide the highest IoU with the ground truth boxes.

Here’s a simplified example to illustrate how the center point offset and IoU threshold work together:

import torch

# Example ground truth box (x_center, y_center, width, height)
gt_box = torch.tensor([0.5, 0.5, 0.4, 0.4])

# Example anchors (width, height)
anchors = torch.tensor([[0.3, 0.3], [0.5, 0.5], [0.7, 0.7]])

# Calculate IoU between ground truth box and anchors
def calculate_iou(box1, box2):
    inter = torch.min(box1[2:], box2[2:]) - torch.max(box1[:2], box2[:2])
    inter = torch.clamp(inter, min=0)
    inter_area = inter[0] * inter[1]
    box1_area = box1[2] * box1[3]
    box2_area = box2[2] * box2[3]
    union_area = box1_area + box2_area - inter_area
    return inter_area / union_area

ious = [calculate_iou(gt_box, torch.cat((torch.tensor([0.5, 0.5]), anchor))) for anchor in anchors]

# Assign anchor with highest IoU
best_anchor_idx = torch.argmax(torch.tensor(ious))
print(f"Best anchor index: {best_anchor_idx}, IoU: {ious[best_anchor_idx]}")

In this example, the anchor with the highest IoU is selected for the ground truth box. If the IoU for the green cell does not meet the threshold, it will not be assigned.

For more detailed information, you can refer to the YOLOv5 Architecture documentation.

If you have any further questions or need additional clarification, feel free to ask! 😊

[galik1999]

Hi,

Thank you for the great and friendly explanations.

Could you please explain two things for me shortly?

What is the purpose of the lambdas in the loss function?
How do the three different outputs of YOLOv5 translate into the final prediction?

Thanks again for your amazing work and super easy and useful documentation.

[pderrenger]

@galik1999 thank you for your kind words.

The lambdas in the loss function serve as weighting factors to balance the contributions of different loss components (classification, objectness, and localization) to the total loss. This helps in fine-tuning the model's performance by emphasizing or de-emphasizing certain aspects of the prediction.

YOLOv5 outputs three different feature maps corresponding to different scales (P3, P4, P5). These outputs allow the model to detect objects of varying sizes more effectively. Each output is processed to generate final predictions, which are then combined and filtered based on confidence scores and non-maximum suppression to produce the final detection results.

For more detailed information, you can refer to the Ultralytics YOLOv5 Architecture documentation.