Problem while Fine-Tuning YoloV8 model about ultralytics HOT 2 OPEN

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 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 Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

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Hello,

Thank you for reaching out and providing detailed information about your issue. It seems like you're experiencing a drop in mAP50 scores after fine-tuning your YOLOv8 model with additional data. Let's work through this step-by-step to identify potential causes and solutions.

Steps to Diagnose and Resolve the Issue

1. Verify Package Versions:
   Ensure you are using the latest versions of torch and ultralytics. You can update them using the following commands:

   pip install --upgrade torch ultralytics

2. Reproducible Code Example:
   To better assist you, could you please provide a minimum reproducible code example? This will help us replicate the issue on our end. You can refer to our guide on creating a minimum reproducible example here. This step is crucial for us to investigate and provide a solution.

3. Training and Fine-Tuning Process:
   Let's ensure that the fine-tuning process is correctly set up. When fine-tuning, it's important to use the same data configuration and ensure that the new data is properly integrated. Here’s an example of how you might structure your training and fine-tuning commands:

   # Initial Training
   !yolo task=detect mode=train model="yolov8n.pt" data="data1.yaml" epochs=100 imgsz=640 batch=16 optimizer=AdamW
   
   # Fine-Tuning
   !yolo task=detect mode=train model="best.pt" data="data2.yaml" epochs=100 imgsz=640 batch=16 optimizer=AdamW

4. Data Consistency:
   Ensure that the new dataset (data2.yaml) is consistent with the original dataset (data1.yaml). The class names and structure should match exactly. Any discrepancies can lead to performance drops.

5. Learning Rate and Optimizer:
   Fine-tuning often requires a lower learning rate to avoid catastrophic forgetting. Consider reducing the learning rate during fine-tuning. You can adjust this in your command:

   !yolo task=detect mode=train model="best.pt" data="data2.yaml" epochs=100 imgsz=640 batch=16 optimizer=AdamW lr0=0.001

6. Validation:
   Validate your model after fine-tuning to ensure that it is performing as expected on both the old and new datasets. This can help identify if the issue is specific to the new data or a general problem.

Example Validation Command

# Validate the fine-tuned model
!yolo task=detect mode=val model="best.pt" data="data2.yaml" imgsz=640

Additional Resources

For more detailed guidance on fine-tuning and best practices, you might find our training documentation helpful.

Please provide the reproducible code example and any additional details you can share. This will help us assist you more effectively.

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