Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks
Summary
This study investigates Parameter-Efficient Fine-Tuning (PEFT) methods, specifically adapters and Low-Rank Adaptation (LoRA), for transformer-based models in instance segmentation tasks. Researchers applied these techniques to two models across four benchmark datasets, demonstrating that fine-tuning only 1-6% of model parameters achieves competitive performance, a significant reduction from the 40-55% required by traditional methods. Key findings indicate that 2-3 adapters per transformer block offer an optimal balance of performance and efficiency. Furthermore, LoRA, when applied to deformable attention for the first time, exhibits strong parameter efficiency and can surpass adapter configurations in certain cases. The impact of PEFT techniques varies based on dataset complexity and model architecture.
Key takeaway
For Machine Learning Engineers optimizing large pretrained models for instance segmentation, this research highlights a critical shift. You should consider adopting Parameter-Efficient Fine-Tuning (PEFT) methods like adapters or LoRA, which reduce fine-tuning parameters to 1-6% compared to traditional 40-55%. This enables scalable and computationally efficient transfer learning, allowing you to deploy powerful models with significantly less resource overhead. Evaluate 2-3 adapters per transformer block or LoRA on deformable attention for optimal results.
Key insights
Parameter-Efficient Fine-Tuning (PEFT) with adapters and LoRA drastically reduces parameters for instance segmentation while maintaining performance.
Principles
- PEFT reduces fine-tuning parameters to 1-6%.
- 2-3 adapters per transformer block optimize efficiency.
- LoRA on deformable attention offers strong efficiency.
Method
Investigates adapters and LoRA on two transformer-based models across four benchmark datasets, integrating sequential adapters and applying LoRA to deformable attention.
In practice
- Apply LoRA to deformable attention.
- Utilize 2-3 adapters per transformer block.
- Tailor PEFT based on dataset complexity.
Topics
- Parameter-Efficient Fine-Tuning
- Instance Segmentation
- Large Pretrained Models
- Adapters
- Low-Rank Adaptation
- Deformable Attention
- Transformer Models
Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.