Face Analysis based on Deep Learning#

With the advancement of deep learning technology, the performance of face analysis has also improved.
I will introduce a high-performing open-source face analysis library and explain how to use it.

InsightFace: 2D and 3D Face Analysis Project#

• https://github.com/deepinsight/insightface

※ License : Please refer to the above site !

Here, we share various models, and I would like to introduce the buffalo_l model pack.
buffalo_l provides blob box, key points, detection score, landmark 2D/3D, gender, age, embedding, and pose information.
 
I will demonstrate the process of face analysis using Python.

LoRA (Low-Rank Adaptation)#

LoRA is one of the PEFT (Parameter-Efficient Fine-Tuning) techniques.
This technique efficiently fine-tunes large pre-trained models for specific tasks.

(The following content is referenced from the paper “LoRA: Low-Rank Adaptation of Large Language Models.”)

Background/Problem#

Models like LLM (Large Language Models) have an extremely large number of parameters.
For example, the llama3 model, released in April 2024, has about 70 billion parameters and a file size of over 40GB, with many models being even larger.
Full fine-tuning of such large models requires high-performance GPUs and considerable training time.
Additionally, fully fine-tuning the base model may potentially degrade the fundamental performance learned during pretraining.