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Open-weight model · Image classification
by PyTorch Image Models timm/tf_efficientnetv2_s.in21k_ft_in1k
tf_efficientnetv2_s.in21k_ft_in1k is an open-weight model for image classification from PyTorch Image Models, released under Apache License 2.0. It has 22M 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. It draws 1.7M downloads a month.
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.
What it takes to serve tf_efficientnetv2_s.in21k_ft_in1k (22M 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 16, 2026.
tf_efficientnetv2_s.in21k_ft_in1k on every accelerator the SAVRN Index prices, at every precision
22M parameters, 173.2 MB of weights, and memory that rounds to zero at every precision — EfficientNetV2-S in its ImageNet-21k-pretrained, ImageNet-1k-fine-tuned build, the recipe encoded in the in21k_ft_in1k tag. The $1.85-an-hour MI300X in the Runs On table is a rounding exercise; this is CPU or edge hardware territory. It draws 1.8M downloads a month and was refreshed 2025-01-21. The repo is 5 files, with weights in bin and safetensors.
Apache 2.0 permits commercial use with notices kept. The page lists training on ImageNet-21k then ImageNet-1k and points to arXiv:2104.00298 and the timm model-results page for per-dataset metrics — read those before committing, since 1k fine-tuned scores overstate real-world robustness. Pair it with resnet18.a1_in1k as the accuracy-versus-latency brackets for your classification pipeline and move up within timm when accuracy starts to pay.
By PyTorch Image Models, published under apache-2.0, revision ea9abc143ea2.
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.
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('tf_efficientnetv2_s.in21k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
5 files, 173.2 MB in total. The weights are 2 files totalling 173.2 MB in bin, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 86.5 MB | 6f1933fb6c0d |
| pytorch_model.bin | Weights | 86.7 MB | 40ed79ccf7aa |
| config.json | Configuration | 638 B | — |
| README.md | Documentation | 4.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
Released by PyTorch Image Models through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 173.2 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.
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (22M parameters) plus a working margin. A long context needs more.
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.
Yes. tf_efficientnetv2_s.in21k_ft_in1k is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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