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

GitHub Repository

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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

Code for paper entitled "Improving Novelty Detection using the Reconstructions of Nearest Neighbours"

Image segmentation with private İstanbul Dataset

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