kerasformers/xception71_tf_in1k

See our collection for all versions of Xception.

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

kerasformers/xception71_tf_in1k

Paper: Xception: Deep Learning with Depthwise Separable Convolutions (arXiv:1610.02357) · HF Papers

Xception interprets Inception modules as depthwise separable convolutions. Classifier or entry/middle/exit-flow backbone.

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

Pure-Keras 3 conversion of timm/xception71.tf_in1k for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (XceptionImageClassify / XceptionModel).

✨ Quick start

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

from PIL import Image
import numpy as np
from kerasformers.models.xception import XceptionImageClassify, XceptionModel

model = XceptionImageClassify.from_weights("kerasformers/xception71_tf_in1k")
backbone = XceptionModel.from_weights(
    "kerasformers/xception71_tf_in1k", as_backbone=True
)

image = Image.open("your_image.jpg").convert("RGB")
image = image.resize((224, 224))
x = np.asarray(image, dtype="float32")[None]  # (1, H, W, 3)
print(model(x).shape)  # (1, num_classes)
feats = backbone(x)
print(len(feats), [tuple(f.shape) for f in feats])

Load any Xception variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub
xception41_tf_in1k	kerasformers/xception41_tf_in1k
xception41p_ra3_in1k	kerasformers/xception41p_ra3_in1k
xception65_ra3_in1k	kerasformers/xception65_ra3_in1k
xception65_tf_in1k	kerasformers/xception65_tf_in1k
xception65p_ra3_in1k	kerasformers/xception65p_ra3_in1k
xception71_tf_in1k	kerasformers/xception71_tf_in1k

Tips

• Set KERAS_BACKEND before importing Keras / kerasformers.
• XceptionImageClassify returns class logits; XceptionModel returns features (as_backbone=True for multi-scale stages).
• See docs and Loading Weights.
• Upstream / timm checkpoints: XceptionImageClassify.from_weights("hf:timm/xception71.tf_in1k").

Special Thanks

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

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