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Sponsored by
YouArt
zeromodels / tf_efficientnet_b6_ns_jft_in1k
Model's Last Updated: August 30 2026
image-classification
Introduction of tf_efficientnet_b6_ns_jft_in1k
Model Details of tf_efficientnet_b6_ns_jft_in1k
See our collection for all versions of EfficientNet.
Run EfficientNet with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/tf_efficientnet_b6_ns_jft_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_b6.ns_jft_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_b6_ns_jft_in1k")
processor = EfficientNetImageProcessor.from_weights("zeromodels/tf_efficientnet_b6_ns_jft_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_b6_ns_jft_in1k", as_backbone=True)
features = backbone(pixels, training=False)
Load any EfficientNet variant the same way with
from_weights("zeromodels/<variant>")
:
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. -
EfficientNetImageClassifyreturns class logits;EfficientNetModelreturns features (as_backbone=Truefor multi-scale stages). - See docs and Loading Weights .
-
Upstream / timm checkpoints:
EfficientNetImageClassify.from_weights("hf:timm/tf_efficientnet_b6.ns_jft_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).
Runs of zeromodels tf_efficientnet_b6_ns_jft_in1k on huggingface.co
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More Information About tf_efficientnet_b6_ns_jft_in1k huggingface.co Model
Url of tf_efficientnet_b6_ns_jft_in1k
tf_efficientnet_b6_ns_jft_in1k huggingface.co Url
Provider of tf_efficientnet_b6_ns_jft_in1k huggingface.co
Other API from zeromodels
Updated:August 27 2026
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