Xception is a deep convolutional neural network that extends the Inception architecture by replacing standard convolutions with depthwise separable convolutions. This modification significantly reduces computational complexity while maintaining high accuracy. Xception introduces extreme Inception modules that completely separate channel and spatial correlations. The architecture achieves state-of-the-art performance in image classification tasks, offering an efficient alternative to traditional CNNs. Its design is particularly suitable for applications requiring both high accuracy and computational efficiency.
| GPU | IXUCA SDK | Release |
|---|---|---|
| BI-V150 | 4.2.0 | 25.03 |
| BI-V100 | 2.2.0 | 22.09 |
Sign up and login in ImageNet official website, then choose 'Download' to
download the whole ImageNet dataset. Specify /path/to/imagenet to your ImageNet path in later training process.
The ImageNet dataset path structure should look like:
imagenet
├── train
│ └── n01440764
│ ├── n01440764_10026.JPEG
│ └── ...
├── train_list.txt
├── val
│ └── n01440764
│ ├── ILSVRC2012_val_00000293.JPEG
│ └── ...
└── val_list.txtpip3 install torch torchvision# One single GPU
python3 train.py --data-path /path/to/imagenet --model xception
# Multiple GPUs on one machine
python3 -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --data-path /path/to/imagenet --model xception