metadata
library_name: lucid
license: apache-2.0
tags:
- image-classification
- xception
- lucid
datasets:
- imagenet-1k
pipeline_tag: image-classification
model-index:
- name: xception
results:
- task:
type: image-classification
dataset:
name: ImageNet-1k
type: imagenet-1k
metrics:
- type: acc@1
value: 79
Xception
Chollet, 2017 — Xception: Deep Learning with Depthwise Separable Convolutions (arXiv:1610.02357)
Lucid port of timm/legacy_xception.tf_in1k,
converted to Lucid-native safetensors.
Available weights
| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
TF_IN1K (default) |
79.0 | — | 22.9M | — | 87.42 MB | timm |
Usage
import lucid.models as models
from lucid.models.weights import XceptionWeights
# default tag
model = models.xception_cls(pretrained=True)
# explicit tag (enum or string)
model = models.xception_cls(weights=XceptionWeights.TF_IN1K)
model = models.xception_cls(pretrained="TF_IN1K")
# preprocessing travels with the weights
weights = XceptionWeights.TF_IN1K
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
Conversion
Converted from timm/legacy_xception.tf_in1k via
python -m tools.convert_weights xception --tag TF_IN1K.
Key mapping + numerical parity verified against the source.
License
apache-2.0 — inherited from the original weights.
Citation
@inproceedings{chollet2017xception,
title={Xception: Deep Learning with Depthwise Separable Convolutions},
author={Chollet, Fran\c{c}ois},
booktitle={CVPR}, year={2017}
}