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Resnet50

Developed by glasses
ResNet50 is an image classification model based on deep residual learning, which solves the vanishing gradient problem in deep neural networks through residual connections.
Image Classification
Transformers
Open Source License:Apache-2.0#Deep Residual Network#Customizable Structure#Multi-scale Feature Extraction
Downloads 13
Release Time : 3/2/2022

Model Overview

ResNet50 is a deep convolutional neural network primarily used for image classification tasks. By introducing residual block structures, it enables deeper networks that are easier to train.

Model Features

Residual Connections
The residual block structure addresses the vanishing gradient problem in deep neural networks, enabling deeper and easier-to-train networks.
Multiple Variants
Offers various model variants from ResNet18 to ResNet200 to accommodate different computational resource requirements.
Highly Customizable
Supports customization of stem structures, block modules, shortcut connection methods, and activation functions.
Feature Extraction
Allows easy extraction of intermediate layer features for transfer learning or other computer vision tasks.

Model Capabilities

Image Classification
Feature Extraction

Use Cases

Computer Vision
ImageNet Image Classification
Performs 1000-class image classification on the ImageNet dataset
Achieves approximately 76% top-1 accuracy on the ImageNet validation set
Transfer Learning
Uses pre-trained models as feature extractors for other vision tasks
Applicable to downstream tasks such as object detection and image segmentation
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