license: mit
datasets:
- uoft-cs/cifar10
language:
- en
pipeline_tag: image-classification
tags:
- efficient-inference
- computer-vision
- efficient-ml
- resnet
- cifar10
- pruning
- channel-gating
- neuromorphic
metrics:
- latency
- throughput
- peak-gpu-memory
- parameters
- accuracy
library_name: pytorch
base_model: microsoft/resnet-18
model-index:
- name: NeuroLattice ResNet-18
results:
- task:
type: image-classification
dataset:
name: CIFAR-10
type: cifar10
metrics:
- type: accuracy
value: 91.24
name: Top-1 Accuracy (%)
- type: parameters-reduction-vs-baseline
value: 78.8
name: model-parameters-reduction (%)
- type: speedup-vs-fp32-baseline
value: 76.2
name: Speedup vs fp32 Baseline (x)
- type: memory-reduction-vs-fp32
value: 83.4
name: Memory Reduction vs fp32 Baseline (%)
- type: latency
value: 0.025
name: inference-latency (ms/sample)
- type: parameters
value: 2373455
name: model-parameter
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.