Thank you for your patience and for providing additional details. I understand that training the YOLOv8 model locally yielded good results, but you are facing issues when training on the compute cluster. Let's delve deeper into the possible causes and solutions.
- Verify Data Integrity:
- Environment & Dependency Differences: Azure Compute Clusters may use different versions of dependencies (CUDA, PyTorch, etc.) compared to your local setup. YOLOv8 is sensitive to environment configurations. Action:
- Compare your local
requirements.txt
with the Azure environment. Use thepip freeze
command locally and cross-check with the Azure cluster’s Conda/Pip setup. - Ensure CUDA/cuDNN versions match (e.g., CUDA 11.x for PyTorch 1.10+).
- Reference: Azure ML Environment Management.
- Compare your local
- Annotation Checks: Validate that annotations (bounding boxes, labels) are correctly formatted and not corrupted. You can use the following script to visualize dataset samples:
from ultralytics.yolo.data.utils import visualize_dataset_samples visualize_dataset_samples('dataset.yaml')
- Class Imbalance: Check for missing or mislabeled classes. Ensure all classes in annotations match the YAML configuration.
- Environment & Dependency Differences: Azure Compute Clusters may use different versions of dependencies (CUDA, PyTorch, etc.) compared to your local setup. YOLOv8 is sensitive to environment configurations. Action:
- Inspect Training Configuration
- Pretrained Weights: Confirm that the model starts from pretrained weights (e.g.,
yolov8n.pt
). Training from scratch without proper initialization often fails:model = YOLO('yolov8n.pt') # Correct model = YOLO('yolov8n.yaml') # Incorrect (unless intentional)
- Hyperparameters: Validate the learning rate (
lr0
), batch size, and epochs. Start with default values (e.g.,lr0=0.01
). Adjust if gradients vanish/explode (check training logs). - Verbose Logging for Debugging: Add
verbose=True
to your YOLOv8 training command:model.train(..., verbose=True)
- Pretrained Weights: Confirm that the model starts from pretrained weights (e.g.,
- Debug with Minimal Examples
- Overfit a Small Subset: Train on 10–20 images for 5–10 epochs. If the loss doesn’t drop, there’s a fundamental issue (e.g., data not loading).
- Data Loader Inspection: Use a script to verify data loading:
dataset = model.train_data # Access loaded dataset print(dataset.names) # Class names for batch in dataset.train_loader: print(batch['img'].shape, batch['cls'].shape) break
- Analyze Training Logs
- Loss Components: Check if
box_loss
,cls_loss
, anddfl_loss
are updating. Flat losses across all components suggest no learning. - Warnings/Errors: Look for CUDA errors, NaN values, or OOM (Out-of-Memory) issues in logs.
- Logging and Output Directory Permissions: If the Azure cluster lacks write permissions to the output directory, metrics may not be saved. Action:
- Mount the output directory as a
OutputFileDatasetConfig
or use Azure’s default./outputs
folder (auto-uploads to the workspace). Example:from azureml.core import Workspace, Dataset ws = Workspace.from_config() datastore = ws.get_default_datastore() dataset = Dataset.File.from_files(path=(datastore, 'data/'))
- Mount the output directory as a
- Loss Components: Check if
I hope this provides more clarity on how to approach the problem. Please try it out and let me know if any progress, will try our best to help you out.
Thanks