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Open-weight model · Image classification
by PyTorch Image Models timm/convnext_tiny.in12k_ft_in1k
A ConvNeXt image classification model. Pretrained in timm on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman. ImageNet-12k training done on TPUs thanks to support of the TRC program.
What it takes to serve convnext_tiny.in12k_ft_in1k (29M 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 | 0.1 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 Sep 18, 2026.
We keep a short list of models that cost effectively nothing to host, and this ConvNeXt classifier from PyTorch Image Models sits near the top of it. Twenty-nine million parameters, 229 MB on disk, 0.1 GB of memory at 16-bit and a rounded 0.0 GB at 8-bit or 4-bit. The Index's cheapest option is one MI300X with 192 GB at $1.85 an hour, and this model would not notice it was there; run it on whatever GPU already handles image intake. It classifies into the ImageNet-1k label set after pretraining on ImageNet-12k, an 11,821-class subset of ImageNet-22k.
Apache 2.0 allows commercial use and modification and adds an express patent grant; keep the license, copyright and NOTICE files and state significant changes. Two checks: the library is timm rather than transformers, and the publisher's timing figures came from PyTorch 1.13 on an RTX 3090, so measure throughput on your own cards.
By PyTorch Image Models, published under apache-2.0, revision aa096f03029c.
A ConvNeXt image classification model. Pretrained in timm on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman.
ImageNet-12k training done on TPUs thanks to support of the TRC program.
Fine-tuning performed on 8x GPU Lambda Labs cloud instances.
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('convnext_tiny.in12k_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, 228.8 MB in total. The weights are 2 files totalling 228.8 MB in bin, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 114.4 MB | a1aefa409b51 |
| pytorch_model.bin | Weights | 114.4 MB | 1abc6ff66688 |
| config.json | Configuration | 662 B | — |
| README.md | Documentation | 16.0 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 | 228.8 MB |
| 16-bit | 0.1 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 (29M 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. convnext_tiny.in12k_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.
A ConvNeXt image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. Explore the dataset and runtime metrics of this model in timm model results. All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
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