Summary of Ptq4sam: Post-training Quantization For Segment Anything, by Chengtao Lv et al.
PTQ4SAM: Post-Training Quantization for Segment Anything
by Chengtao Lv, Hong Chen, Jinyang Guo, Yifu Ding, Xianglong Liu
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Segment Anything Model (SAM) has achieved impressive performance in many computer vision tasks, but its large scale hinders practical deployment due to immense memory and computation costs. To address this issue, we propose a post-training quantization framework for SAM, namely PTQ4SAM. This framework tackles two key challenges: the bimodal distribution in post-Key-Linear activations and the variations in post-Softmax distributions resulting from different attention mechanisms. We introduce a Bimodal Integration strategy to transform the bimodal distribution into an easier-to-quantize normal distribution offline, and an Adaptive Granularity Quantization for Softmax through searching the optimal power-of-two base. Our experimental results across various vision tasks, datasets, and model variants demonstrate the superiority of PTQ4SAM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Segment Anything Model (SAM) is a computer program that can do many things with images. But it needs too much memory and computing power to work well in real life. To fix this problem, scientists created a new way to make SAM use less memory and energy while still doing its job well. They made two special parts: one helps SAM understand different types of information, and the other makes sure SAM can still do its tasks correctly. |
Keywords
» Artificial intelligence » Attention » Quantization » Sam » Softmax
