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kerasformers/
inception_resnet_v2_tf_in1k

Name: inception_resnet_v2_tf_in1k
Availability: InStock

timm/inception_resnet_v2.tf_in1k

finetune

apache-2.0

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See our collection for all versions of Inception-ResNet-V2.

Run Inception-ResNet-V2 with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

zeromodels/inception_resnet_v2_tf_in1k

Paper: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (arXiv:1602.07261) · HF Papers

Inception-ResNet-V2 combines Inception modules with residual connections. Classifier or backbone.

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

Pure-Keras 3 conversion of timm/inception_resnet_v2.tf_in1k for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (InceptionResNetV2ImageClassify / InceptionResNetV2Model).

✨ Quick start

python

import os

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

from PIL import Image
from zeromodels.models.inception_resnetv2 import InceptionResNetV2ImageClassify, InceptionResNetV2Model, InceptionResNetV2ImageProcessor

model = InceptionResNetV2ImageClassify.from_weights("zeromodels/inception_resnet_v2_tf_in1k")
processor = InceptionResNetV2ImageProcessor.from_weights("zeromodels/inception_resnet_v2_tf_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 = InceptionResNetV2Model.from_weights("zeromodels/inception_resnet_v2_tf_in1k", as_backbone=True)
features = backbone(pixels, training=False)

Load any Inception-ResNet-V2 variant the same way with from_weights("zeromodels/<variant>"):

Variant	Hub
`inception_resnet_v2_tf_ens_adv_in1k`	`zeromodels/inception_resnet_v2_tf_ens_adv_in1k`
`inception_resnet_v2_tf_in1k`	`zeromodels/inception_resnet_v2_tf_in1k`

Tips

Set KERAS_BACKEND before importing Keras / zeromodels.
InceptionResNetV2ImageClassify returns class logits; InceptionResNetV2Model returns features (as_backbone=True for multi-scale stages).
See docs and Loading Weights.
Upstream / timm checkpoints: InceptionResNetV2ImageClassify.from_weights("hf:timm/inception_resnet_v2.tf_in1k").

Special Thanks

A huge thank you to the Inception-ResNet-V2 authors and the timm / Hub communities for creating and releasing these models.

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

kerasformers/inception_resnet_v2_tf_in1k