rtdetr weight problem

[S-7-12]

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Question

For the rtdetr model, I would like to train the model using the backbones of resnet50 and 101, but have not found the corresponding pre-training weights.

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[glenn-jocher]

Hello! For training RT-DETR models with ResNet backbones, pre-trained weights might not be directly available from the Ultralytics repository. You can utilize pre-trained ResNet models from PyTorch repository as an alternative. Here's a code snippet on how you can do this:

import torch
from ultralytics import YOLO

# Load RT-DETR model and specify the backbone
model = YOLO('rtdetr.yaml', backbone='resnet50')  # Change to 'resnet101' as needed

# Load pre-trained weights from PyTorch
pretrained_weights = torch.load('resnet50.pth')  # Adjust path and filename as necessary
model.load_state_dict(pretrained_weights, strict=False)

# Continue with your training setup

Replace 'resnet50.pth' with the file path to your downloaded PyTorch weights, and ensure your YAML configuration file (rtdetr.yaml) correctly configures the model to use the desired ResNet backbone. Hope this helps! 😊

[S-7-12]

Traceback (most recent call last):
model = YOLO('rtdetr.yaml', backbone='resnet50') # Change to 'resnet101' as needed
TypeError: init() got an unexpected keyword argument 'backbone'
The parameter backbone does not seem to be set when the model is initialized

[S-7-12]

from ultralytics import RTDETR

# Load RT-DETR model and specify the backbone
model = RTDETR('ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml') 

# Train the model on the COCO8 example dataset for 100 epochs
model.train（

）

I noticed that the rtdetr r-50.yaml file already exists in the ultralytics repository and can be trained that way, but the weighting issue is still not resolved.

[glenn-jocher]

Hi there! It's great to see that you've found the appropriate rtdetr-resnet50.yaml file for training. If you're encountering issues with loading the specific weights for the ResNet-50 backbone in the RT-DETR model, you should first ensure that you have the correct pre-trained weights available. If they're not present, you might need to download them separately.

For training with pre-trained ResNet weights, you'll have to load these weights manually before the training starts. Here's a simple way to do it using PyTorch:

import torch
from ultralytics import RTDETR

# Load the RTDETR model configuration
model = RTDETR('ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml')

# Load pre-trained weights
weights = torch.load('path_to_resnet50_weights.pth')
model.model.load_state_dict(weights, strict=False)

# Train the model
model.train(data='path_to_coco128.yaml', epochs=100)

Make sure to replace 'path_to_resnet50_weights.pth' with the path to your ResNet-50 PyTorch weights file, and adjust the data path and epochs as necessary. This should resolve the issue with the weights not being loaded properly. Let us know if you need further assistance! 😊

[yingyu13]

@glenn-jocher ,hi, the pre-trained ResNet weights cannot be loaded correctly, as the parameter names of pre-trained ResNet models from PyTorch repository is consistent with backbone defined in rtdetr r-50.yaml of ultralytics repository. How to resolve this issue?

[S-7-12]

Hi there! It's great to see that you've found the appropriate rtdetr-resnet50.yaml file for training. If you're encountering issues with loading the specific weights for the ResNet-50 backbone in the RT-DETR model, you should first ensure that you have the correct pre-trained weights available. If they're not present, you might need to download them separately.

For training with pre-trained ResNet weights, you'll have to load these weights manually before the training starts. Here's a simple way to do it using PyTorch:

import torch
from ultralytics import RTDETR

# Load the RTDETR model configuration
model = RTDETR('ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml')

# Load pre-trained weights
weights = torch.load('path_to_resnet50_weights.pth')
model.model.load_state_dict(weights, strict=False)

# Train the model
model.train(data='path_to_coco128.yaml', epochs=100)

Make sure to replace 'path_to_resnet50_weights.pth' with the path to your ResNet-50 PyTorch weights file, and adjust the data path and epochs as necessary. This should resolve the issue with the weights not being loaded properly. Let us know if you need further assistance! 😊

#12663 (comment)
There is indeed this problem, there is indeed a problem with the code you provided, it really doesn't load the backbone weights successfully, hopefully you can give a solution for this

[glenn-jocher]

@S-7-12 hi! Thanks for pointing out the issue with loading the backbone weights. It seems like the parameter names in the pre-trained ResNet model might not match directly with those expected by the RT-DETR model configuration. To resolve this, you can map the ResNet weights to the expected names in the RT-DETR model.

Here’s a quick way to adapt the weights:

import torch
from ultralytics import RTDETR

# Load the RTDETR model configuration
model = RTDETR('ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml')

# Load pre-trained weights
resnet_weights = torch.load('path_to_resnet50_weights.pth')
adapted_weights = {k if 'backbone' in k else 'backbone.' + k: v for k, v in resnet_weights.items()}

# Load adapted weights into the model, ignoring incompatible keys
model.model.load_state_dict(adapted_weights, strict=False)

# Now you can proceed with training
model.train(data='path_to_coco128.yaml', epochs=100)

This script prepends 'backbone.' to the keys of the ResNet weights, aligning them with the expected format in the RT-DETR model. Adjust the string manipulation as necessary based on the exact mismatch pattern. Let me know if this helps or if you need further adjustments! 😊

[S-7-12]

``> @S-7-12 hi! Thanks for pointing out the issue with loading the backbone weights. It seems like the parameter names in the pre-trained ResNet model might not match directly with those expected by the RT-DETR model configuration. To resolve this, you can map the ResNet weights to the expected names in the RT-DETR model.

Here’s a quick way to adapt the weights:

import torch
from ultralytics import RTDETR

# Load the RTDETR model configuration
model = RTDETR('ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml')

# Load pre-trained weights
resnet_weights = torch.load('path_to_resnet50_weights.pth')
adapted_weights = {k if 'backbone' in k else 'backbone.' + k: v for k, v in resnet_weights.items()}

# Load adapted weights into the model, ignoring incompatible keys
model.model.load_state_dict(adapted_weights, strict=False)

# Now you can proceed with training
model.train(data='path_to_coco128.yaml', epochs=100)

This script prepends 'backbone.' to the keys of the ResNet weights, aligning them with the expected format in the RT-DETR model. Adjust the string manipulation as necessary based on the exact mismatch pattern. Let me know if this helps or if you need further adjustments! 😊

import warnings
import torch
warnings.filterwarnings('ignore')

from ultralytics import RTDETR

# Load RT-DETR model and specify the backbone
model = RTDETR('ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml') # Change to 'resnet101' as needed
# Display model information (optional)


# Train the model on the COCO8 example dataset for 100 epochs
model.train(data='/home/rendoudou/swx/pycharm/v5-ours/yolov5-master/data/our.yaml', 
                      imgsz=640,
                      cache=False,
                      epochs=300,
                      pretrained='resnet101.pth',
                      batch=16,
                      device='1',
                      project='runs-test/rtdetr-r101-train',
                      name='rtdetr-ours-',
                      patience=30,
                      )

Thanks, I found out that I can introduce weights this way, the code is currently running with no error messages, do you see any errors in the code?

[glenn-jocher]

Hey @S-7-12-12! Glad to hear that you've managed to get your code running smoothly! 🚀 From the snippet you've shared, everything looks in order. Just ensure that the path to your dataset and the pretrained weights are correctly specified. Also, keep an eye on the device configuration to match your available hardware. If any issues arise during training or if you need further assistance, feel free to reach out. Happy training! 😊