Model distillation for yolov5

[anazkhan]

Search before asking

• I have searched the YOLOv5 issues and discussions and found no similar questions.

Question

Hello,
can you please guide me on how to perform model distillation for yolov5 where yolov5n is the student model and yolov5l is the teacher model . it will be helpful if you can explain the steps in detail and the code involved as i am not able to find a proper guide online.
Thanks in Advance

Additional

No response

[github-actions]

👋 Hello @anazkhan, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

Requirements

Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

[glenn-jocher]

@anazkhan hello,

Thank you for reaching out with your question on model distillation for YOLOv5! Model distillation is a powerful technique to transfer knowledge from a larger, more complex model (teacher) to a smaller, more efficient model (student). In your case, you want to distill knowledge from YOLOv5l (teacher) to YOLOv5n (student).

While we don't have a dedicated guide for model distillation in YOLOv5, I can provide you with a general approach and some code snippets to get you started.

Steps for Model Distillation

1. Load Teacher and Student Models:
   First, load the pre-trained teacher and student models using PyTorch.

   import torch
   
   # Load teacher model (YOLOv5l)
   teacher_model = torch.hub.load('ultralytics/yolov5', 'yolov5l', pretrained=True)
   
   # Load student model (YOLOv5n)
   student_model = torch.hub.load('ultralytics/yolov5', 'yolov5n', pretrained=True)

2. Prepare the Dataset:
   Use the same dataset for both models. Ensure your dataset is properly formatted and ready for training.

3. Define the Distillation Loss:
   Combine the standard YOLOv5 loss with a distillation loss. The distillation loss typically includes a term for the difference between the teacher's and student's outputs (logits).

   from torch.nn import functional as F
   
   def distillation_loss(student_outputs, teacher_outputs, targets, alpha=0.5, temperature=3.0):
       # Standard YOLOv5 loss (you may need to adapt this to your specific loss function)
       standard_loss = F.mse_loss(student_outputs, targets)
   
       # Distillation loss
       distillation_loss = F.kl_div(
           F.log_softmax(student_outputs / temperature, dim=1),
           F.softmax(teacher_outputs / temperature, dim=1),
           reduction='batchmean'
       ) * (temperature ** 2)
   
       # Combine losses
       return alpha * standard_loss + (1 - alpha) * distillation_loss

4. Training Loop:
   Implement the training loop where you compute the outputs of both the teacher and student models, then apply the distillation loss.

   optimizer = torch.optim.Adam(student_model.parameters(), lr=1e-4)
   
   for epoch in range(num_epochs):
       for images, targets in dataloader:
           # Forward pass
           student_outputs = student_model(images)
           with torch.no_grad():
               teacher_outputs = teacher_model(images)
   
           # Compute loss
           loss = distillation_loss(student_outputs, teacher_outputs, targets)
   
           # Backward pass and optimization
           optimizer.zero_grad()
           loss.backward()
           optimizer.step()
   
       print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")

Additional Resources

For more detailed information on setting up your environment and running YOLOv5, please refer to our YOLOv5 Quickstart Tutorial.

If you encounter any issues or have further questions, feel free to provide a minimum reproducible code example so we can assist you better. You can find more information on creating a reproducible example here.

I hope this helps you get started with model distillation for YOLOv5! If you have any more questions, feel free to ask.

[anazkhan]

Thank you for the guidance!
Also, how can i load the same training data mentioned in data/coco128.yaml folder into the dataloader to train the student model as well.

[glenn-jocher]

Hello @anazkhan,

You're welcome! I'm glad to hear that the guidance was helpful. To load the training data specified in the data/coco128.yaml file into the dataloader for training your student model, you can follow these steps:

Steps to Load Training Data

1. Ensure Dataset Configuration:
   Make sure your coco128.yaml file is correctly configured. This file should specify the paths to your training and validation datasets.

   train: ../coco128/images/train2017  # path to training images
   val: ../coco128/images/val2017  # path to validation images
   nc: 80  # number of classes
   names: ../coco128/coco.names  # path to class names

2. Load Dataset Using YOLOv5's Built-in Functionality:
   YOLOv5 provides a convenient way to load datasets using the LoadImagesAndLabels class. Here’s how you can integrate it into your training loop:

   from yolov5.utils.datasets import LoadImagesAndLabels
   from yolov5.utils.general import check_dataset
   
   # Load dataset configuration
   data_config = check_dataset('data/coco128.yaml')
   
   # Create dataloader
   dataloader = LoadImagesAndLabels(data_config['train'], img_size=640, batch_size=16, augment=True)

3. Integrate Dataloader into Training Loop:
   Use the dataloader in your training loop to fetch batches of images and targets.

   from torch.utils.data import DataLoader
   
   # Create DataLoader
   train_loader = DataLoader(dataloader, batch_size=16, shuffle=True, collate_fn=dataloader.collate_fn)
   
   # Training loop
   for epoch in range(num_epochs):
       for images, targets, paths, _ in train_loader:
           # Forward pass
           student_outputs = student_model(images)
           with torch.no_grad():
               teacher_outputs = teacher_model(images)
   
           # Compute loss
           loss = distillation_loss(student_outputs, teacher_outputs, targets)
   
           # Backward pass and optimization
           optimizer.zero_grad()
           loss.backward()
           optimizer.step()
   
       print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")

Additional Tips

• Verify Dependencies: Ensure you are using the latest versions of torch and YOLOv5 from the Ultralytics repository.
• Reproducibility: If you encounter any issues, please provide a minimum reproducible code example as outlined here. This will help us investigate and resolve any potential bugs more efficiently.

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

[anazkhan]

just want to clarify, 'from yolov5.utils.datasets import LoadImagesAndLabels' was module not found error but the same module was present in from yolov5.utils.dataloaders import LoadImagesAndLabels.

[anazkhan]

data_config = check_dataset('data/data.yaml')
dataloader = LoadImagesAndLabels(data_config['train'], img_size=640, batch_size=16, augment=True)

train_loader = DataLoader(dataloader, batch_size=16, shuffle=True, collate_fn=dataloader.collate_fn)

This is how i have created the data loader to train the student model.
following is the error i am getting while running the training loop.

[glenn-jocher]

Hello @anazkhan,

Thank you for providing the code snippet and the error screenshot. It looks like you encountered an issue while creating the dataloader for training your student model. Let's address this step-by-step.

Verify Module Import

First, you correctly noted that the LoadImagesAndLabels class should be imported from yolov5.utils.dataloaders:

from yolov5.utils.dataloaders import LoadImagesAndLabels

Check Dataset Configuration

Ensure that your data.yaml file is correctly formatted and paths are valid. Here’s an example structure:

train: ../coco128/images/train2017  # path to training images
val: ../coco128/images/val2017  # path to validation images
nc: 80  # number of classes
names: ../coco128/coco.names  # path to class names

Create Dataloader

Here’s a refined version of your dataloader setup:

from yolov5.utils.general import check_dataset
from yolov5.utils.dataloaders import LoadImagesAndLabels
from torch.utils.data import DataLoader

# Load dataset configuration
data_config = check_dataset('data/data.yaml')

# Create dataloader
dataloader = LoadImagesAndLabels(data_config['train'], img_size=640, batch_size=16, augment=True)

# Create DataLoader
train_loader = DataLoader(dataloader, batch_size=16, shuffle=True, collate_fn=dataloader.collate_fn)

Error Handling

If you encounter an error, please ensure you are using the latest versions of torch and YOLOv5 from the Ultralytics repository. If the issue persists, providing a minimum reproducible code example will help us investigate further. You can find more details on creating a reproducible example here.

Training Loop

Here’s a sample training loop for reference:

optimizer = torch.optim.Adam(student_model.parameters(), lr=1e-4)

for epoch in range(num_epochs):
    for images, targets, paths, _ in train_loader:
        # Forward pass
        student_outputs = student_model(images)
        with torch.no_grad():
            teacher_outputs = teacher_model(images)

        # Compute loss
        loss = distillation_loss(student_outputs, teacher_outputs, targets)

        # Backward pass and optimization
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")

Next Steps

If the error persists, please share the specific error message and a minimum reproducible code example. This will help us diagnose the issue more effectively.

Thank you for your patience and cooperation. If you have any further questions, feel free to ask! 😊

[anazkhan]

I am getting the same error message once again , i believe this is occuring because of the dataloader. kindy look into it .
sharing my yaml file and the error message below.

[glenn-jocher]

Hello @anazkhan,

Thank you for sharing the details of your issue. I understand that encountering errors can be frustrating, and I'm here to help you resolve this as efficiently as possible.

Steps to Resolve the Issue

1. Verify Dataset Configuration:
   Ensure that your data.yaml file is correctly formatted and paths are valid. Here’s an example structure:

   train: ../coco128/images/train2017  # path to training images
   val: ../coco128/images/val2017  # path to validation images
   nc: 80  # number of classes
   names: ../coco128/coco.names  # path to class names

2. Check for Latest Versions:
   Please verify that you are using the latest versions of torch and YOLOv5 from the Ultralytics repository. This ensures you have the latest bug fixes and features.

3. Minimum Reproducible Example:
   To help us investigate further, could you please provide a minimum reproducible code example? This will allow us to reproduce the issue on our end and identify the root cause. You can find more details on creating a reproducible example here.

Example Dataloader Setup

Here’s a refined version of your dataloader setup for reference:

from yolov5.utils.general import check_dataset
from yolov5.utils.dataloaders import LoadImagesAndLabels
from torch.utils.data import DataLoader

# Load dataset configuration
data_config = check_dataset('data/data.yaml')

# Create dataloader
dataloader = LoadImagesAndLabels(data_config['train'], img_size=640, batch_size=16, augment=True)

# Create DataLoader
train_loader = DataLoader(dataloader, batch_size=16, shuffle=True, collate_fn=dataloader.collate_fn)

Training Loop Example

Here’s a sample training loop for reference:

optimizer = torch.optim.Adam(student_model.parameters(), lr=1e-4)

for epoch in range(num_epochs):
    for images, targets, paths, _ in train_loader:
        # Forward pass
        student_outputs = student_model(images)
        with torch.no_grad():
            teacher_outputs = teacher_model(images)

        # Compute loss
        loss = distillation_loss(student_outputs, teacher_outputs, targets)

        # Backward pass and optimization
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")

Next Steps

Please provide the minimum reproducible code example and ensure you are using the latest versions of the required libraries. This will greatly assist us in diagnosing and resolving the issue.

Thank you for your cooperation and patience. If you have any further questions or need additional assistance, feel free to ask! 😊