Detects age group with about 59% accuracy based on an image. See https://www.kaggle.com/code/dima806/age-group-image-classification-vit for details.
A vision transformer finetuned to classify the age of a given person's face.
Parameters86M
Context—
Weights686.5 MB
License—
AccessOpen weights
Monthly Downloads238.6k
Runs On
What it takes to serve vit-age-classifier (86M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also 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
A vision transformer finetuned to classify the age of a given person's face.
Excerpt from the card by Nate Raw.
Configuration
- Architecture
- ViTForImageClassification
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Model type
- vit
Identity and Version
- Repository
- nateraw/vit-age-classifier
- Publisher
- Nate Raw
- Task
- Image classification
- Modality
- Image
- Library
- transformers
- Parameters
- 86M parameters
- Languages
- vit
- Revision
- 4b5acc345d9692c3ff4f45d250531afd3a3da0ef
- First published
- 2022-03-02
- Last updated
- 2024-07-28
Files and Weights
6 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 · 1.0 KB
Documentation1 file · 951 B
Repository1 file · 744 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 343.2 MB | 44813db5205a |
| pytorch_model.bin | Weights | 343.3 MB | 4dc2e2d6b7c0 |
| config.json | Configuration | 850 B | — |
| preprocessor_config.json | Configuration | 197 B | — |
| README.md | Documentation | 951 B | — |
| .gitattributes | Repository | 744 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 686.5 MB
Download from Nate Raw
Released by Nate Raw through its official repository on Hugging Face.
Built From
- Trained on (disclosed) nateraw/fairface
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 686.5 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About vit-age-classifier
How much GPU memory does vit-age-classifier 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 vit-age-classifier 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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