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DenseNet-121

Model Download Download (with sample test data) ONNX version Opset version Top-1 accuracy (%)
DenseNet-121 32 MB 33 MB 1.1 3
DenseNet-121 32 MB 33 MB 1.1.2 6
DenseNet-121 32 MB 33 MB 1.2 7
DenseNet-121 32 MB 33 MB 1.3 8
DenseNet-121 32 MB 33 MB 1.4 9
DenseNet-121-12 32 MB 30 MB 1.9 12 60.96
DenseNet-121-12-int8 9 MB 6 MB 1.9 12 60.20

Compared with the DenseNet-121-12, DenseNet-121-12-int8's op-1 accuracy drop ratio is 1.25% and performance improvement is 1.18x.

Note the performance depends on the test hardware.

Performance data here is collected with Intel® Xeon® Platinum 8280 Processor, 1s 4c per instance, CentOS Linux 8.3, data batch size is 1.

Description

DenseNet-121 is a convolutional neural network for classification.

Paper

Densely Connected Convolutional Networks

Dataset

ILSVRC2012

Source

Caffe2 DenseNet-121 ==> ONNX DenseNet

Model input and output

Input

data_0: float[1, 3, 224, 224]

Output

fc6_1: float[1, 1000, 1, 1]

Pre-processing steps

Post-processing steps

Sample test data

random generated sampe test data:

  • test_data_0.npz
  • test_data_1.npz
  • test_data_2.npz
  • test_data_set_0
  • test_data_set_1
  • test_data_set_2

Results/accuracy on test set

Quantization

Mask R-CNN R-50-FPN-int8 is obtained by quantizing Mask R-CNN R-50-FPN-fp32 model. We use Intel® Neural Compressor with onnxruntime backend to perform quantization. View the instructions to understand how to use Intel® Neural Compressor for quantization.

Environment

onnx: 1.9.0 onnxruntime: 1.10.0

Prepare model

wget https://github.com/onnx/models/raw/main/vision/classification/densenet-121/model/densenet-12.onnx

Model quantize

bash run_tuning.sh --input_model=path/to/model \  # model path as *.onnx
                   --config=densenet.yaml \
                   --output_model=path/to/save

References

Contributors

License

MIT