tf_efficientnet_b2_in1k

See our collection for all versions of EfficientNet.

Run EfficientNet with Keras 3: JAX, PyTorch, or TensorFlow

zeromodels/tf_efficientnet_b2_in1k

Paper: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (arXiv:1905.11946) · HF Papers

EfficientNet compound-scales depth/width/resolution for strong accuracy/efficiency. Classifier or multi-scale MBConv backbone.

For more details on the model, please go to the upstream model card.

Pure-Keras 3 conversion of timm/tf_efficientnet_b2.in1k for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (EfficientNetImageClassify / EfficientNetModel).

✨ Quick start

import os

os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from PIL import Image
from zeromodels.models.efficientnet import EfficientNetImageClassify, EfficientNetModel, EfficientNetImageProcessor

model = EfficientNetImageClassify.from_weights("zeromodels/tf_efficientnet_b2_in1k")
processor = EfficientNetImageProcessor.from_weights("zeromodels/tf_efficientnet_b2_in1k")

image = Image.open("your_image.jpg").convert("RGB")
pixels = processor(image)  # resize + normalize (normalization lives in the processor)
logits = model(pixels, training=False)
print(logits.shape)  # (1, num_classes)

# Feature extraction: the backbone without the classifier head
backbone = EfficientNetModel.from_weights("zeromodels/tf_efficientnet_b2_in1k", as_backbone=True)
features = backbone(pixels, training=False)

Load any EfficientNet variant the same way with from_weights("zeromodels/<variant>"):

Tips

• Set KERAS_BACKEND before importing Keras / zeromodels.
• EfficientNetImageClassify returns class logits; EfficientNetModel returns features (as_backbone=True for multi-scale stages).
• See docs and Loading Weights.
• Upstream / timm checkpoints: EfficientNetImageClassify.from_weights("hf:timm/tf_efficientnet_b2.in1k").

Special Thanks

A huge thank you to the EfficientNet authors and the timm / Hub communities for creating and releasing these models.

License: see YAML license (usually matches the upstream checkpoint).