resnet50-cifar100-a100

DIA-MVP/resnet50-cifar100-a100

resnet50-cifar100-a100 is a image classification model from DIA-MVP. Use it when you need a label for an image. The card lists the license as apache-2.0.

A demo model from the Data & Impact Accounting (DIA) lab. It performs image classification (CIFAR-100) via full fine-tune, with the base model microsoft/resnet-50, trained on NVIDIA A100.

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Public

Updated Jun 29, 2026

Repo size

95.2 MB

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Hugging Face

Repo makeup

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.pt95.2 MB · 100%

At a glance

Task: Image Classification
License: apache-2.0
Access: Public
Created: Jun 29, 2026
Updated: Jun 29, 2026
SHA: 0930d077

Base models

microsoft/resnet-50

Model card

From the Hugging Face model README

ResNet-50 — CIFAR-100 (NVIDIA A100)

A demo model from the Data & Impact Accounting (DIA) lab. It performs image classification (CIFAR-100) via full fine-tune, with the base model microsoft/resnet-50, trained on NVIDIA A100.

The point of this repo is not the model itself but its dia_report — a standardized record of the energy, carbon, and water used to train it, embedded in this card's metadata.

This footprint feeds the DIA dashboard, which rolls up a base model and all its derivatives to show the cumulative carbon, water, and energy cost of a model family.

Training footprint

Metric	Value
Hardware	1× NVIDIA A100-SXM4-80GB
Compute	0.6684 GPU-hours
Energy	0.1369 (measured) kWh
Carbon	0.0089 (measured) kgCO₂eq
Water	0.246–0.548 (estimated-from-default-wue) L
Grid region	ca-on

Energy and carbon are measured with CodeCarbon; water is estimated from a default water-usage-effectiveness range. Carbon uses the local grid's intensity (Ontario, ~0.03 kgCO₂eq/kWh).

Reproduce

REPO=DIA-MVP/resnet50-cifar100-a100 python scripts/train_resnet50_cifar.py