# resnet50.fb_swsl_ig1b_ft_in1k by PyTorch Image Models Source: https://savrn.com/models/resnet50-fb-swsl-ig1b-ft-in1k Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve resnet50.fb_swsl_ig1b_ft_in1k (26M 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. [resnet50.fb_swsl_ig1b_ft_in1k on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/resnet50-fb-swsl-ig1b-ft-in1k/gpus) ## Model Card A ResNet-B image classification model. single layer 7x7 convolution with pooling 1x1 convolution shortcut downsample Pretrained on Instagram-1B hashtags dataset using semi-weakly supervised learning and fine-tuned on ImageNet-1k by paper authors. - Billion-scale semi-supervised learning for image classification: https://arxiv.org/abs/1905.00546 - 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. Excerpt from the card by PyTorch Image Models, licensed cc-by-nc-4.0. ## Identity and Version Repository timm/resnet50.fb_swsl_ig1b_ft_in1k Publisher PyTorch Image Models Task Image classification Modality Image Library timm Parameters 26M parameters Languages Not stated by the source Revision db15d80e1377d8389fa2ab973174175dd36b8a57 First published 2023-04-05 Last updated 2025-01-21 ## Files and Weights 5 files, 205.1 MB in total. The weights are 2 files totalling 205.0 MB in bin, safetensors. Weights2 files · 205.0 MB Configuration1 file · 695 B Documentation1 file · 38.5 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 102.5 MB | b6fc5693ac93 | | pytorch_model.bin | Weights | 102.5 MB | fabde3499514 | | config.json | Configuration | 695 B | — | | README.md | Documentation | 38.5 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License cc-by-nc-4.0 Access Open weights, no gate Download size 205.0 MB [Download from PyTorch Image Models](https://huggingface.co/timm/resnet50.fb_swsl_ig1b_ft_in1k) Released by PyTorch Image Models through its official repository on Hugging Face. [Read the license](https://creativecommons.org/licenses/by-nc/4.0/). ## Built From - Described by [arXiv:1512.03385](https://savrn.com/papers/deep-residual-learning-for-image-recognition) - Described by arXiv:1905.00546 ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 205.0 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 resnet50.fb_swsl_ig1b_ft_in1k ### How much GPU memory does resnet50.fb_swsl_ig1b_ft_in1k need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (26M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run resnet50.fb_swsl_ig1b_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 resnet50.fb_swsl_ig1b_ft_in1k commercially? Not without separate permission. resnet50.fb_swsl_ig1b_ft_in1k is released under Creative Commons Attribution-NonCommercial 4.0. CC BY-NC 4.0 permits sharing and adapting with credit for non-commercial purposes only. Commercial use needs separate permission from the rights holder. ## Similar Models Model · Image classification ### [resnet50.a1_in1k](https://savrn.com/models/resnet50-a1-in1k) [PyTorch Image Models](https://savrn.com/model-publishers/timm) 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. Open weights apache-2.0 26M parameters timm [View model](https://savrn.com/models/resnet50-a1-in1k) Model · Image classification ### [resnet-50](https://savrn.com/models/resnet-50) [Microsoft](https://savrn.com/model-publishers/microsoft) ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models. This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference… Open weights apache-2.0 26M parameters transformers [View model](https://savrn.com/models/resnet-50) Model · Image classification ### [resnet50d.ra2_in1k](https://savrn.com/models/resnet50d-ra2-in1k) [PyTorch Image Models](https://savrn.com/model-publishers/timm) A ResNet-D image classification model. 3-layer stem of 3x3 convolutions with pooling 2x2 average pool + 1x1 convolution shortcut downsample 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 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187 Explore the dataset and runtime metrics of… Open weights apache-2.0 26M parameters timm [View model](https://savrn.com/models/resnet50d-ra2-in1k) Model · Image classification ### [resnet50_gn.a1h_in1k](https://savrn.com/models/resnet50-gn-a1h-in1k) [PyTorch Image Models](https://savrn.com/model-publishers/timm) 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. Based on ResNet Strikes Back A1 recipe Stronger dropout, stochastic depth, and RandAugment than paper 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. Open weights apache-2.0 26M parameters timm [View model](https://savrn.com/models/resnet50-gn-a1h-in1k) Model · Image classification ### [cspnext_l.rsb_a1_in1k](https://savrn.com/models/cspnext-l-rsb-a1-in1k) [Munehiro Kobayashi](https://savrn.com/model-publishers/munehiro-k) A CSPNeXt image classification model. Pretrained on ImageNet-1k by OpenMMLab (RTMDet) and converted to timm format. Name disambiguation: This is the CSPNeXt backbone of RTMDet (OpenMMLab), as implemented in MMDetection. A separate paper, CSPNeXt: A new efficient token hybrid backbone (Chen et al., EAAI 2024, doi:10.1016/j.engappai.2024.107886), uses the same name for a different architecture. These weights do not implement that paper; please cite RTMDet (below) for this model. - https://github.com/open-mmlab/mmdetection/tree/main/configs/rtmdet/classification - https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnextrsbpretrain/cspnext-l8xb256-rsb-a1-600ein1k-6a760974.pth ImageNet-1k… Open weights apache-2.0 27M parameters timm [View model](https://savrn.com/models/cspnext-l-rsb-a1-in1k) Model · Image classification ### [swin-tiny-patch4-window7-224-finetuned-eurosat](https://savrn.com/models/swin-tiny-patch4-window7-224-finetuned-eurosat) [User Name](https://savrn.com/model-publishers/username-6370) This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 128 - lrschedulertype: linear - numepochs: 3 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 28M parameters transformers [View model](https://savrn.com/models/swin-tiny-patch4-window7-224-finetuned-eurosat) ## PyTorch Image Models [All models and datasets](https://savrn.com/model-publishers/timm) ## Versions - [db15d80e1377](https://savrn.com/models/resnet50-fb-swsl-ig1b-ft-in1k/versions/db15d80e1377) · current 2026-09-18 ## Explore More - [All image classification models](https://savrn.com/models/tasks/image-classification) - [All models under cc-by-nc-4.0](https://savrn.com/models/licenses/cc-by-nc-4-0) - [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/resnet50.fb_swsl_ig1b_ft_in1k) - [How the hub is built](https://savrn.com/model-hub/methodology)