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Medai Resnet50 Brain
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Medai Resnet50 Brain

Developed by aryan-anand
ResNet-50 is a deep residual network developed by Microsoft Research, widely used for image classification tasks.
Image ClassificationOpen Source License:MIT#Image Classification#Deep Residual Network#High-Accuracy Recognition
Downloads 31
Release Time : 4/7/2025

Model Overview

ResNet-50 is a classic convolutional neural network architecture that addresses the vanishing gradient problem in deep networks through residual connections, making it suitable for image classification tasks.

Model Features

Residual Connections
Effectively solves the vanishing gradient problem in deep networks through residual connections, enabling deeper and easier-to-train networks.
High Performance
Delivers outstanding performance on multiple image classification benchmark datasets, such as ImageNet.
Broad Applicability
Suitable for various image classification tasks, including but not limited to object recognition and scene classification.

Model Capabilities

Image Classification
Object Recognition
Feature Extraction

Use Cases

Computer Vision
ImageNet Classification
Performs image classification across 1,000 categories on the ImageNet dataset.
Top-1 accuracy ~76%, Top-5 accuracy ~93%.
Medical Image Analysis
Used for lesion detection and classification in medical imaging.
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