Bottleneck Transformers for Visual Recognition

Last update: Jan 03, 2023

Overview

Bottleneck Transformers for Visual Recognition

Experiments

Model	Params (M)	Acc (%)
ResNet50 baseline (ref)	23.5M	93.62
BoTNet-50	18.8M	95.11%
BoTNet-S1-50	18.8M	95.67%
BoTNet-S1-59	27.5M	95.98%
BoTNet-S1-77	44.9M	wip

Summary

Usage (example)

Model

from model import Model

model = ResNet50(num_classes=1000, resolution=(224, 224))
x = torch.randn([2, 3, 224, 224])
print(model(x).size())

Module

from model import MHSA

resolution = 14
mhsa = MHSA(planes, width=resolution, height=resolution)

Reference

Paper link
Author: Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani
Organization: UC Berkeley, Google Research

Owner

Myeongjun Kim

Computer Vision Research using Deep Learning

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Bottleneck Transformers for Visual Recognition

Related tags

Overview

Bottleneck Transformers for Visual Recognition

Experiments

Summary

Usage (example)

Reference

Owner

Myeongjun Kim

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