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Open-weight model · Image classification

gender-classification

by Rizvan Dwikifirdaus rizvandwiki/gender-classification

Autogenerated by HuggingPics Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo.

Parameters86M
Context—
Weights686.5 MB
License—
AccessOpen weights
Monthly Downloads1.4M

Runs On

What it takes to serve gender-classification (86M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.2 GB 0.2 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.1 GB 0.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.0 GB 0.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

SAVRN's Notes on gender-classification

This one was autogenerated by HuggingPics, and the page reflects it: a ViT with 12 layers and 86M parameters, stored in float32, built to sort images by gender, with no evaluations reported and no training data named. At 16-bit the weights take 0.2 GB, which is also the total needed, and 8-bit halves that to 0.1 GB. The cheapest setup on our Index, one MI300X with 192 GB at $1.85 an hour on-demand, is far more card than this asks for, so share it with other image models.

The license field is empty: no summary, no commercial-use answer, and open access to the files does not change that. Resolve it with the publisher before any deployment; until then treat this as evaluation-only. Note too the 2023-05-18 last update and the absence of any named base model or dataset, which leaves nothing to audit the training against.

SAVRN Research, 2026-09-18

Model Card

Autogenerated by HuggingPics Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo.

Excerpt from the card by Rizvan Dwikifirdaus.

Configuration

Architecture
ViTForImageClassification
Layers
12
Hidden size
768
Feed-forward size
3,072
Attention heads
12
Stored precision
float32
Model type
vit

Identity and Version

Repository
rizvandwiki/gender-classification
Publisher
Rizvan Dwikifirdaus
Task
Image classification
Modality
Image
Library
transformers
Parameters
86M parameters
Languages
vit
Revision
380b17658cc037b246aab0b62a20deb7dd607e65
First published
2022-12-06
Last updated
2023-05-18

Files and Weights

10 files, 686.6 MB in total. The weights are 2 files totalling 686.5 MB in bin, safetensors.

Weights2 files · 686.5 MB
Configuration2 files · 1.1 KB
Documentation1 file · 747 B
Other4 files · 67.7 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights343.2 MB 6ac5c0298533
pytorch_model.binWeights343.3 MB 049c8ee0e892
config.jsonConfiguration727 B —
preprocessor_config.jsonConfiguration325 B —
README.mdDocumentation747 B —
images/female.jpgOther35.1 KB 7c5095faa4f6
images/male.jpgOther29.5 KB 802366c93c74
runs/events.out.tfevents.1670316283.4ed123ecb4eaOther1.1 KB 72219caf78cd
runs/events.out.tfevents.1670320068.4ed123ecb4eaOther2.0 KB 0f236c6cdfd4
.gitattributesRepository1.6 KB —

License and Download

License
Not stated by the source
Access
Open weights, no gate
Download size
686.5 MB
Download from Rizvan Dwikifirdaus

Released by Rizvan Dwikifirdaus through its official repository on Hugging Face.

Memory Requirements

PrecisionWeights in memory
As published686.5 MB
16-bit0.2 GB
8-bit0.1 GB
4-bit0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Compare gender-classification

Questions About gender-classification

How much GPU memory does gender-classification need?

About 0.2 GB at 16-bit and 0.1 GB at 4-bit: the weights (86M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run gender-classification on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

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