Summary
The issue at hand is fine-tuning a model to detect a specific object (hat) on a person without losing the pre-trained weights that identify a person. This is a common problem in transfer learning and object detection tasks. The goal is to retain the person class from the yolov11 model while improving the detection of hats.
Root Cause
The root cause of this issue is:
- Insufficient training data for the hat class
- Overwriting pre-trained weights during the fine-tuning process
- Inadequate hyperparameter tuning for the new task
Why This Happens in Real Systems
This issue occurs in real systems because:
- Object detection models are often pre-trained on large datasets with a specific set of classes
- Fine-tuning these models for new tasks can be challenging, especially when the new task has a different set of classes or objects
- Retaining pre-trained knowledge while adapting to new tasks is a difficult problem in machine learning
Real-World Impact
The real-world impact of this issue is:
- Reduced accuracy in detecting persons in videos
- Ineffective hat detection due to insufficient training data
- Increased training time and computational resources required to retrain the model
Example or Code (if necessary and relevant)
from ultralytics import YOLO
# Load pre-trained yolov11 model
model = YOLO("yolov11.pt")
# Freeze pre-trained weights for person class
for name, param in model.named_parameters():
if "person" in name:
param.requires_grad = False
# Fine-tune the model for hat detection
model.train()How Senior Engineers Fix It
Senior engineers fix this issue by:
- Freezing pre-trained weights for the person class
- Using transfer learning to adapt the model to the new task
- Hyperparameter tuning to optimize the model for the new task
- Collecting and annotating more training data for the hat class
Why Juniors Miss It
Juniors may miss this issue because:
- Lack of experience with transfer learning and object detection tasks
- Insufficient understanding of pre-trained models and their limitations
- Inadequate knowledge of hyperparameter tuning and its importance in machine learning