Aligned Xception

Category: CNN · Code: timm/models/xception_aligned.py · Architectures: 5 · Pretrained weights: 6 (6 with ImageNet-1k results)

Aligned Xception (41, 65, 71 layers) as modified in DeepLab, implemented from scratch and compatible with the TensorFlow DeepLab weights.

Papers

• Xception: Deep Learning with Depthwise Separable Convolutions
• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Usage

See the Quickstart for loading pretrained weights, image preprocessing and fine-tuning, and Feature Extraction for using the models as backbones.

>>> import timm
>>> model = timm.create_model('xception41p.ra3_in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)

Model API support

✓ supported, ✓ (square) works for square sizes only, ✗ not supported, – not needed (the model already accepts any input size). Checked by running each feature on a randomly initialized model (XceptionAligned with xception41p at 299), other architectures of the same class are expected to behave the same. See the Model API page for details.

Architectures

GMACs and parameter counts are for the default input size and classifier of each architecture, from the benchmark results.

Pretrained weights

Top-1 is ImageNet-1k validation accuracy from results-imagenet.csv at the listed eval size, - if the weight has no ImageNet-1k result (e.g. pretraining or image-text weights). Classes is the size of the pretrained classifier, none for weights without one.

Weights are grouped by source: the paper or origin given in the pretrained config, else a Hub organization other than timm, else the first part of the pretrained tag (architecture.tag). Sources with a single weight are listed under other.

ra3 tags

tf tags