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Open-weight model · Image classification
by PyTorch Image Models timm/resnet18.a3_in1k
A ResNet-B image classification model. single layer 7x7 convolution with pooling 1x1 convolution shortcut downsample Trained on ImageNet-1k in timm using recipe template described below.
What it takes to serve resnet18.a3_in1k (12M 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 Sep 18, 2026.
Not every model on this hub is a language model. The timm resnet18.a3_in1k is a 12M-parameter ResNet-B image classifier trained on ImageNet-1k with the A3 recipe from ResNet Strikes Back, and its weight files total 93.6 MB. Memory needed rounds to 0.0 GB at every precision on our table. The cheapest listed rental, one MI300X at $1.85 per hour, is the wrong tool; a classifier this size belongs on hardware you already have.
Apache 2.0 allows commercial use, so it can ship inside a product or an inspection pipeline if the license and copyright notices stay intact. The weights come in both safetensors and pytorch formats, so confirm which one your serving stack loads. And the recipe is documented in arXiv:2110.00476, with the architecture in arXiv:1512.03385, which is where to look before fine-tuning on your own label set.
By PyTorch Image Models, published under apache-2.0, revision 033fbbcc3d67.
A ResNet-B image classification model.
This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample
Trained on ImageNet-1k in timm using recipe template described below.
Recipe details:
* ResNet Strikes Back A3 recipe
* LAMB optimizer with BCE loss
* Cosine LR schedule with warmup
5 files, 93.7 MB in total. The weights are 2 files totalling 93.6 MB in bin, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 46.8 MB | f049dd3c2e08 |
| pytorch_model.bin | Weights | 46.8 MB | 1c56bc7a90dd |
| config.json | Configuration | 756 B | — |
| README.md | Documentation | 38.4 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 | 93.6 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 (12M 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. resnet18.a3_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 ResNet-B image classification model. single layer 7x7 convolution with pooling 1x1 convolution shortcut downsample Trained on ImageNet-1k in timm using recipe template described below. ResNet Strikes Back A1 recipe - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 Explore the dataset and runtime metrics of this model in timm model results.
A EfficientNet image classification model. Trained on ImageNet-1k in timm using recipe template described below. RandAugment RA2 recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as B recipe in ResNet Strikes Back. RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging Step (exponential decay w/ staircase) LR schedule with warmup Explore the dataset and runtime metrics of this model in timm model results.
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). 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 Explore the dataset and runtime metrics of this model in timm model results.
A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-1k. 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.
A DenseNet image classification model. Pretrained on ImageNet-1k in timm by Ross Wightman using RandAugment RA recipe. Related to B recipe in ResNet Strikes Back.
A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-1k. 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.