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Open-weight model · Image classification
by PyTorch Image Models timm/repvgg_a0.rvgg_in1k
repvgg_a0.rvgg_in1k is an open-weight model for image classification from PyTorch Image Models, released under MIT License. It has 9M parameters. At 16-bit it needs about 0 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 597.3k downloads a month.
A RepVGG image classification model. Trained on ImageNet-1k by paper authors. This model architecture is implemented using timm's flexible BYOBNet (Bring-Your-Own-Blocks Network).
What it takes to serve repvgg_a0.rvgg_in1k (9M 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 | 0.0 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 9, 2026.
repvgg_a0.rvgg_in1k on every accelerator the SAVRN Index prices, at every precision
RepVGG is what you deploy when you want a plain VGG-style convnet with modern accuracy: this a0 checkpoint is 9M parameters, 1.5 GMACs at 224 pixels, and 73.2 MB on disk. Weights and working memory both round to 0.0 GB at every precision, so the only real question is images per second on hardware you already own; a dedicated MI300X at $1.85 an hour makes no sense for it.
MIT covers commercial use, modification and redistribution with a notice obligation. The checkpoint was trained on ImageNet-1k by the paper authors and is implemented in timm's BYOBNet, which also gives per-stage feature extraction if you want a backbone rather than a classifier. Anything outside the ImageNet-1k label set means a fine-tune. The page cites arXiv:2101.03697, first published 2023-08-23, last updated 2025-01-21, with 597.3k downloads a month.
By PyTorch Image Models, published under mit, revision e292d220aa8b.
A RepVGG image classification model. Trained on ImageNet-1k by paper authors.
This model architecture is implemented using timm's flexible BYOBNet (Bring-Your-Own-Blocks Network).
BYOBNet allows configuration of: * block / stage layout * stem layout * output stride (dilation) * activation and norm layers * channel and spatial / self-attention layers
...and also includes timm features common to many other architectures, including:
* stochastic depth
* gradient checkpointing
* layer-wise LR decay
* per-stage feature extraction
5 files, 73.2 MB in total. The weights are 2 files totalling 73.2 MB in bin, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 36.6 MB | 2b2bfd7feff2 |
| pytorch_model.bin | Weights | 36.7 MB | d8d1275b0e8a |
| config.json | Configuration | 645 B | — |
| README.md | Documentation | 4.5 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 | 73.2 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 GB at 16-bit and 0 GB at 4-bit: the weights (9M 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. repvgg_a0.rvgg_in1k is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
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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).
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