efficientnet-b7

Model overview

efficientnet-b7 is published by Google as an image-classification checkpoint. Captured configuration identifies EfficientNetForImageClassification with model type efficientnet, and hub tags record ImageNet-1k association. According to the model card, it was introduced in EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Tan and Le.

Recorded capabilities

Use cases in the source record

• Raw image classification into one of 1,000 ImageNet classes, which the model card lists as the raw-model use, with a documented EfficientNetImageProcessor and Transformers workflow.
• Starting point for task-specific fine-tunes discovered through the model hub, which the model card points to for interested tasks.

Limitations and unknowns

• The model card carries a disclaimer that the releasing team did not write it and that it was written by the Hugging Face team, so publisher attribution should be read with that provenance.
• No evaluation results were extracted from this record.
• No parameter count, quantization, or context-window value was extracted.
• Provider state is historical snapshot data, not independently refreshed current availability.

Source and provenance

Source: google/efficientnet-b7

Captured: Unknown. Processed: 2026-09-07T19:34:45.829617+00:00.

EfficientNet (b7 model) EfficientNet model trained on ImageNet-1k at resolution 600x600. It was introduced in the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le, and first released in this repository . Disclaimer: The team releasing EfficientNet did not write a model card for this model so this model card has been written by the Hugging Face team. Model description EfficientNet is a mobile friendly pure convolutional model (ConvNet) that proposes a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compou…