ResNet152 Open-source Image Classification Model - Solve the Vanishing Gradient Problem and Easily Achieve Accurate Classification

Resnet152

Developed by glasses

ResNet152 is an image classification model based on deep residual learning, which solves the gradient vanishing problem in deep network training through residual connections.

Image Classification

Transformers

Open Source License:Apache-2.0 #Deep Residual Network #Customizable Structure #Multi-scale Feature Extraction

Downloads 14

Release Time : 3/2/2022

Model Overview

ResNet152 is a deep convolutional neural network specifically designed for image recognition tasks. By introducing residual learning mechanisms, it allows the network to be deeper and easier to train.

Model Features

Residual Connections

Solves the gradient vanishing problem in deep networks through skip connections, enabling deeper networks.

Multiple Variants

Offers various depth variants from ResNet18 to ResNet200 to meet different needs.

Highly Customizable

Supports custom stem structures, block modules, and shortcut methods.

Feature Extraction

Easily extracts intermediate layer features, suitable for tasks like transfer learning.

Model Capabilities

Image Classification

Feature Extraction

Transfer Learning

Use Cases

Computer Vision

Image Classification

Performs image classification on large datasets like ImageNet.

Achieves high accuracy on ImageNet.

Transfer Learning

Used as a pre-trained model for other vision tasks.

Significantly improves downstream task performance.

🚀 ResNet152

ResNet152 is an implementation of the ResNet architecture proposed in Deep Residual Learning for Image Recognition, which is widely used in image classification tasks.

🚀 Quick Start

ResNet152 is a powerful model for image classification. It is based on the ResNet architecture proposed in the paper Deep Residual Learning for Image Recognition.

✨ Features

Multiple Variants: The implementation provides various ResNet variants, including resnet18, resnet26, resnet34, resnet50, resnet101, resnet152, and resnet200.
Customizable: You can customize the model by changing the activation function, number of classes, block type, stem, and shortcut.
Feature Extraction: It allows you to extract features from the encoder.

💻 Usage Examples

Basic Usage

ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()

Variants (d) proposed in `Bag of Tricks for Image Classification with Convolutional Neural Networks <https://arxiv.org/pdf/1812.01187.pdf`_

ResNet.resnet26d()
ResNet.resnet34d()
ResNet.resnet50d()
# You can construct your own one by chaning `stem` and `block`
resnet101d = ResNet.resnet101(stem=ResNetStemC, block=partial(ResNetBottleneckBlock, shortcut=ResNetShorcutD))

Advanced Usage

# change activation
ResNet.resnet18(activation = nn.SELU)
# change number of classes (default is 1000 )
ResNet.resnet18(n_classes=100)
# pass a different block
ResNet.resnet18(block=SENetBasicBlock)
# change the steam
model = ResNet.resnet18(stem=ResNetStemC)
# change shortcut
model = ResNet.resnet18(block=partial(ResNetBasicBlock, shortcut=ResNetShorcutD))
# store each feature
x = torch.rand((1, 3, 224, 224))
# get features
model = ResNet.resnet18()
# first call .features, this will activate the forward hooks and tells the model you'll like to get the features
model.encoder.features
model(torch.randn((1,3,224,224)))
# get the features from the encoder
features = model.encoder.features
print([x.shape for x in features])
#[torch.Size([1, 64, 112, 112]), torch.Size([1, 64, 56, 56]), torch.Size([1, 128, 28, 28]), torch.Size([1, 256, 14, 14])]

📄 License

This project is licensed under the Apache-2.0 license.

📦 Additional Information

Property	Details
Tags	image-classification
Datasets	imagenet

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