A EfficientNet-v2 image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman. Explore the dataset and runtime metrics of this model in timm model results.
Open-weight model · Image classification
resnet34-finetuned-scenario16
by Ivan Richardson RichardsonI/resnet34-finetuned-scenario16
resnet34-finetuned-scenario16 is an open-weight model for image classification from Ivan Richardson. It has 21M parameters. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model.
Parameters21M
Context—
Weights85.2 MB
License—
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve resnet34-finetuned-scenario16 (21M 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 | 0.0 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 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 Oct 22, 2026.
resnet34-finetuned-scenario16 on every accelerator the SAVRN Index prices, at every precision
Model Card
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Excerpt from the card by Ivan Richardson.
Configuration
- Architecture
- ResNetForImageClassification
- Model type
- resnet
Identity and Version
- Repository
- RichardsonI/resnet34-finetuned-scenario16
- Publisher
- Ivan Richardson
- Task
- Image classification
- Modality
- Image
- Library
- transformers
- Parameters
- 21M parameters
- Languages
- Not stated by the source
- Revision
- be1e3ffdad4236ff138c82cf72cd52fb4fd93053
- First published
- 2026-10-07
- Last updated
- 2026-10-07
Files and Weights
8 files, 85.2 MB in total. The weights are 1 file totalling 85.2 MB in safetensors.
Weights1 file · 85.2 MB
Configuration3 files · 3.3 KB
Documentation1 file · 5.2 KB
Other2 files · 234 B
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 85.2 MB | dac561640666 |
| config.json | Configuration | 730 B | — |
| experiment_results/resnet34_results.json | Configuration | 2.3 KB | — |
| preprocessor_config.json | Configuration | 352 B | — |
| README.md | Documentation | 5.2 KB | — |
| experiment_results/class_distribution.csv | Other | 87 B | — |
| experiment_results/hyperparameter_results.csv | Other | 147 B | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 85.2 MB
Download from Ivan Richardson
Released by Ivan Richardson through its official repository on Hugging Face.
Built From
- Described by arXiv:1910.09700
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 85.2 MB |
| 16-bit | 0.0 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About resnet34-finetuned-scenario16
How much GPU memory does resnet34-finetuned-scenario16 need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (21M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run resnet34-finetuned-scenario16 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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