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A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
git clone --recursive https://github.com/PaddlePaddle/PaddleClas.git
cd PaddleClas
yum install mesa-libGL -y
pip3 install -r requirements.txt
pip3 install protobuf==3.20.3
pip3 install urllib3==1.26.13
python3 setup.py install
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.txt
# Make sure your dataset path is the same as above
cd PaddleClas
# Link your dataset to default location
ln -s /path/to/imagenet ./dataset/ILSVRC2012
export FLAGS_cudnn_exhaustive_search=True
export FLAGS_cudnn_batchnorm_spatial_persistent=True
export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -u -m paddle.distributed.launch --gpus=0,1,2,3 tools/train.py -c ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml -o Arch.pretrained=False -o Global.device=gpu
| GPUs | Top1 | Top5 | ips |
|---|---|---|---|
| BI-V100 x 4 | 0.757 | 0.925 | 171 |
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