Model Card — NeuroLattice™ ResNet-18

NeuroLattice™ ResNet-18 is a production-optimized image classification model delivering substantial inference efficiency gains while maintaining high accuracy on CIFAR-10.
The model is designed for enterprise deployment, prioritizing low latency, high throughput, and minimal GPU memory usage under real-world inference workloads.

The results demonstrate clear operational advantages over standard ResNet-18 baselines, validated under identical hardware and evaluation conditions.

Performance Overview

Evaluation Context

• Dataset: CIFAR-10
• Input Resolution: 32 × 32
• Batch Size: 4096
• Samples Evaluated: 10,000
• GPU: NVIDIA GeForce RTX 4050 (Laptop, 6 GB)

How to Get Started with the Model

Installation

pip install -r requirements.txt

Prerequisites

• Python: 3.8 or higher (tested with Python 3.12.7)
• CUDA: Optional, for GPU acceleration (CUDA 11.8+ recommended)

RUN

$env:KMP_DUPLICATE_LIB_OK="TRUE"; python hf_inference_resnet_standalone.py --checkpoint model.pt --batch-size 4096 --evaluate --plot

Model Overview

• Model Name: NeuroLattice™ ResNet-18
• Task: Image Classification
• Dataset: CIFAR-10
• Accuracy: 91.24%
• Inference Precision: FP16
• License: MIT

This model belongs to the ResNet-18 family and is engineered for deterministic, high-efficiency inference.
Design emphasis is placed on scalability, resource efficiency, and predictable runtime performance.

Business Impact

NeuroLattice™ ResNet-18 enables organizations to:

• Reduce infrastructure and GPU memory costs
• Increase inference density per device
• Achieve lower latency without sacrificing accuracy
• Deploy deep learning models in constrained environments

The model is production-ready and designed for seamless integration into existing inference systems.