microsoft/resnet-34
image-classification model by microsoft
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Model card & documentation
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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 makes…
Read the full model card ↗ · Preview checked 2026-09-21T06:42:42.383Z
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Model overview
microsoft/resnet-34 is a image-classification model repository published by microsoft on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-classification
- Library
- transformers
- Recent downloads (30 days)
- 46,043
- Cumulative likes
- 14
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 21,814,696
- Architecture
- ResNetForImageClassification
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2022-03-16T15:41:51.000Z
- Last modified
- 2023-06-26T19:49:23.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-21T06:36:52.646Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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