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zededa/
resnet50-cars

Name: resnet50-cars
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ResNet50 - Stanford Cars Classification

Organization

Framework

Accuracy

This repository contains a pre-trained ResNet50 model, fine-tuned to classify vehicle makes, models, and years using the Stanford Cars dataset. This model is maintained and provided by ZEDEDA.

📊 Model Details

Architecture: ResNet50
Framework: ONNX
Precision: FP32 (Single Precision)
Model Size: 94 MB
Task: Image Classification
Number of Classes: 196

📈 Performance

The model was evaluated on the Stanford Cars validation split and achieved the following performance metrics:

Top-1 Accuracy: 89.39%

🗂️ Dataset Information

This model was trained on the Stanford Cars Dataset.

The dataset contains 16,185 images of 196 classes of cars.
The data is split into 8,144 training images and 8,041 testing images.
Classes are typically at the level of Make, Model, Year (e.g., 2012 Tesla Model S or 2012 BMW M3 coupe).

🚀 Usage

Because the model is saved in the ONNX format, it can be easily deployed using onnxruntime in various environments.

Prerequisites

pip install onnxruntime numpy Pillow

zededa/resnet50-cars