Test Model
ResNet50 v1.5 is an improved version of the original ResNet50 v1 model, achieving approximately 0.5% higher top1 accuracy by adjusting convolution strides.
Downloads 18
Release Time : 3/2/2022
Model Overview
This model is an image classification model based on the ResNet architecture, primarily used for image classification tasks.
Model Features
Improved Convolution Strides
In bottleneck blocks requiring downsampling, v1.5 sets stride = 2 in the 3x3 convolution, improving top1 accuracy by approximately 0.5% compared to v1.
Mixed Precision Training
Supports mixed precision training, leveraging Tensor Core acceleration on Volta, Turing, and NVIDIA Ampere GPU architectures, achieving over 2x training speedup.
Multi-Backend Deployment
Supports deployment via TorchScript, ONNX Runtime, or TensorRT as backends for inference on NVIDIA Triton Inference Server.
Model Capabilities
Image Classification
High-Precision Inference
GPU Acceleration Support
Use Cases
Image Recognition
Animal Recognition
Identify animal species in images, such as tigers, cats, etc.
Highly accurate classification results
Object Recognition
Identify everyday objects, such as teapots, furniture, etc.
Highly accurate classification results
Scene Recognition
Identify architectural or natural scenes, such as palaces, forests, etc.
Highly accurate classification results
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