| --- |
| 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: 79.0 } |
| --- |
| |
| # Xception |
|
|
| > Chollet, 2017 — *Xception: Deep Learning with Depthwise Separable Convolutions* (arXiv:1610.02357) |
|
|
| [Lucid](https://github.com/ChanLumerico/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 |
|
|
| ```python |
| 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} |
| } |
| ``` |
|
|
