Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision
In recent years, model quantization for face recognition has gained prominence. Traditionally, compressing models involved vast datasets like the 5.8 million-image MS1M dataset as well as extensive training times, raising the question of whether such data enormity is essential. This paper addresses this by introducing an efficiency-driven approach, fine-tuning the model with just up to 14,000 images, 440 times smaller than MS1M. We demonstrate that effective quantization is achievable with a smaller dataset, presenting a new paradigm. Moreover, we incorporate an evaluation-based metric loss and achieve an outstanding 96.15% accuracy on the IJB-C dataset, establishing a new state-of-the-art compressed model training for face recognition. The subsequent analysis delves into potential applications, emphasizing the transformative power of this approach. This paper advances model quantization by highlighting the efficiency and optimal results with small data and training time.
Code (0)
등록된 구현이 없습니다.
Tasks
Face RecognitionQuantizationSimilar Papers 제목 키워드 기반
QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization
Deep learning-based face recognition models follow the common trend in deep neural networks by utilizing full-precision floating-point networks with high computational costs. Deploying such networks in use-cases constrai…
Face RecognitionLightweight Face RecognitionQuantizationNow You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos
Face video anonymization is aimed at privacy preservation while allowing for the analysis of videos in a number of computer vision downstream tasks such as expression recognition, people tracking, and action recognition.…
Action RecognitionThe Influence of Streamlined Music on Cognition and Mood
Recent advances in sound engineering have led to the development of so-called streamlined music designed to reduce exogenous attention and improve endogenous attention. Although anecdotal reports suggest that streamlined…
FormReliable Deep Learning for Small-Scale Classifications: Experiments on Real-World Image Datasets from Bangladesh
Convolutional neural networks (CNNs) have achieved state-of-the-art performance in image recognition tasks but often involve complex architectures that may overfit on small datasets. In this study, we evaluate a compact …
Image ClassificationOmnizart: A General Toolbox for Automatic Music Transcription
We present and release Omnizart, a new Python library that provides a streamlined solution to automatic music transcription (AMT). Omnizart encompasses modules that construct the life-cycle of deep learning-based AMT, an…
Chord RecognitionDownbeat TrackingInformation RetrievalMusic Information Retrieval+2