Resnet 50
Microsoft's Resnet 50 is a pre-trained convolutional neural network optimized for vision tasks, specifically image classification, and is licensed as open-source. It is genuinely best at image recognition, having been trained on the ImageNet-1k dataset at a resolution of 224x224. Notably, Resnet 50 utilizes a residual learning architecture with skip connections, enabling the training of deeper models, and this version, v1.5, has a slight accuracy improvement over the original model.
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25.6M
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Estimated GPU requirements for Resnet 50
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Resnet 50
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Microsoft has not published a context window for Resnet 50, so the catalog shows it as not available rather than estimating one.
Yes. Resnet 50 is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Resnet 50 accepts Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Resnet 50 is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Resnet 50 is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 0.4 GB at INT8 for the weights and a default context, from the catalog's 25.6M parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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