Efficientnet B4
EfficientNet is a mobile-friendly pure convolutional model that uniformly scales depth, width, and resolution dimensions, trained on the ImageNet-1k dataset.
Downloads 5,528
Release Time : 2/15/2023
Model Overview
EfficientNet is an efficient convolutional neural network model primarily used for image classification tasks. It optimizes model performance through innovative scaling methods, making it suitable for mobile devices and resource-constrained environments.
Model Features
Compound Scaling Method
Achieves more efficient model scaling by uniformly adjusting depth, width, and resolution dimensions.
Mobile-Friendly
Designed for mobile devices and resource-constrained environments, balancing performance and computational cost.
High-Resolution Processing
Supports high-resolution input of 380x380, improving classification accuracy.
Model Capabilities
Image Classification
Visual Feature Extraction
Use Cases
General Image Recognition
Animal Recognition
Identifies animal species in images, such as tigers, cats, etc.
Accurately classifies 1000 ImageNet categories
Object Recognition
Identifies everyday items, such as teapots, furniture, etc.
Scene Recognition
Identifies types of scenes, such as buildings, natural landscapes, etc.
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