efficientnet-b4

Model overview

efficientnet-b4 is published by google as an image-classification model. The captured configuration identifies EfficientNetForImageClassification with an efficientnet model type. According to the model card, it was trained on ImageNet-1k at 380x380 resolution.

Recorded capabilities

Use cases in the source record

• Raw image classification over the documented 1,000 ImageNet classes.
• Transformers experiments that load the checkpoint with EfficientNetImageProcessor for COCO-style inputs.

Limitations and unknowns

• No parameter count was extracted for this record.
• No evaluation results were extracted from this record.
• Provider state is historical snapshot data, not independently refreshed current availability.
• The model card states it was written by the Hugging Face team, not the releasing team.

Source and provenance

Source: google/efficientnet-b4

Captured: Unknown. Processed: 2026-09-07T19:34:45.817351+00:00.

EfficientNet (b4 model) EfficientNet model trained on ImageNet-1k at resolution 380x380. 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…