# convnext_tiny.fb_in22k_ft_in1k by PyTorch Image Models
Source: https://savrn.com/models/convnext-tiny-fb-in22k-ft-in1k
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

---

## Runs On

What it takes to serve convnext_tiny.fb_in22k_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](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 24, 2026.

[convnext_tiny.fb_in22k_ft_in1k on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/convnext-tiny-fb-in22k-ft-in1k/gpus)

## Model Card

By PyTorch Image Models, published under apache-2.0, revision bc48a87f119b.

### Model card for convnext_tiny.fb_in22k_ft_in1k

A ConvNeXt image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors.

### Model Details

- Model Type: Image classification / feature backbone
- Model Stats:
- Params (M): 28.6
- GMACs: 4.5
- Activations (M): 13.4
- Image size: train = 224 x 224, test = 288 x 288
- Papers:
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- Original: https://github.com/facebookresearch/ConvNeXt
- Dataset: ImageNet-1k
- Pretrain Dataset: ImageNet-22k

### Model Usage

#### Image Classification

```
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.fb_in22k_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)
```

#### Feature Map Extraction

[Read the full model card (897 words)](https://savrn.com/models/convnext-tiny-fb-in22k-ft-in1k/card)

## Identity and Version

Repository

timm/convnext_tiny.fb_in22k_ft_in1k

Publisher

PyTorch Image Models

Task

Image classification

Modality

Image

Library

timm

Parameters

29M parameters

Languages

Not stated by the source

Revision

bc48a87f119bae1b06418f5340bfa97ef75d4609

First published

2022-12-13

Last updated

2025-01-21

## Files and Weights

5 files, 228.8 MB in total. The weights are 2 files totalling 228.8 MB in bin, safetensors.

Weights2 files · 228.8 MB

Configuration1 file · 666 B

Documentation1 file · 15.8 KB

Repository1 file · 1.5 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| model.safetensors | Weights | 114.4 MB | 77b619e187de |
| pytorch_model.bin | Weights | 114.4 MB | 8cb65dc650a7 |
| config.json | Configuration | 666 B | — |
| README.md | Documentation | 15.8 KB | — |
| .gitattributes | Repository | 1.5 KB | — |

## License and Download

License

apache-2.0

Access

Open weights, no gate

Download size

228.8 MB

[Download from PyTorch Image Models](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k)

Released by PyTorch Image Models through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0).

## Built From

- Described by [arXiv:2201.03545](https://savrn.com/papers/a-convnet-for-the-2020s)
- Trained on (disclosed) imagenet-1k
- Trained on (disclosed) imagenet-22k

## Memory Requirements

| 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.

## Questions About convnext_tiny.fb_in22k_ft_in1k

### How much GPU memory does convnext_tiny.fb_in22k_ft_in1k need?

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.

### What is the cheapest GPU to run convnext_tiny.fb_in22k_ft_in1k 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.

### Can I use convnext_tiny.fb_in22k_ft_in1k commercially?

Yes. convnext_tiny.fb_in22k_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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## PyTorch Image Models

[All models and datasets](https://savrn.com/model-publishers/timm)

## Versions

- [bc48a87f119b](https://savrn.com/models/convnext-tiny-fb-in22k-ft-in1k/versions/bc48a87f119b) · current 2026-09-18

## Explore More

- [All image classification models](https://savrn.com/models/tasks/image-classification)
- [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0)
- [Model comparisons](https://savrn.com/models/comparisons)
- [The model directory](https://savrn.com/models)
- [Open model prices by host](https://savrn.com/ai-index/pricing/open-models)

## Source

- Repository metadata, read 2026-10-20.
- [PyTorch Image Models website](https://github.com/huggingface/pytorch-image-models)
- [Hugging Face record](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k)
- [How the hub is built](https://savrn.com/model-hub/methodology)