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Open-weight model · Image classification

gender-classification-2

by Rizvan Dwikifirdaus rizvandwiki/gender-classification-2

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 Downloads140.3k

Runs On

What it takes to serve gender-classification-2 (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.

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-2
Publisher
Rizvan Dwikifirdaus
Task
Image classification
Modality
Image
Library
transformers
Parameters
86M parameters
Languages
vit
Revision
a999f3503a6893e3dc75350dd623f4219c71e3b9
First published
2022-12-12
Last updated
2023-05-18

Files and Weights

11 files, 686.5 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 · 751 B
Other5 files · 55.9 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights343.2 MB 870a9cfb711a
pytorch_model.binWeights343.3 MB 715a9b7b4602
config.jsonConfiguration729 B
preprocessor_config.jsonConfiguration325 B
README.mdDocumentation751 B
images/female.jpgOther24.0 KB 9532c1eac299
images/male.jpgOther28.3 KB fd9bfbd3dec8
runs/events.out.tfevents.1670814677.320584c06762Other661 B a2a7a872ead3
runs/events.out.tfevents.1670817083.320584c06762Other1.8 KB 2b1e5a6e284e
runs/events.out.tfevents.1670943791.52bea202c091Other1.1 KB b9b1137958a3
.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.

Questions About gender-classification-2

How much GPU memory does gender-classification-2 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-2 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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