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README.md

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+---
+language: en
+license: apache-2.0
+model_name: densenet-12-int8.onnx
+tags:
+- validated
+- vision
+- classification
+- densenet-121
+---
+<!--- SPDX-License-Identifier: MIT -->
+
+# DenseNet-121
+
+|Model        |Download  |Download (with sample test data)| ONNX version |Opset version|Top-1 accuracy (%)|
+| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
+|DenseNet-121| [32 MB](model/densenet-3.onnx)  |  [33 MB](model/densenet-3.tar.gz) |  1.1 | 3| |
+|DenseNet-121| [32 MB](model/densenet-6.onnx)  |  [33 MB](model/densenet-6.tar.gz) |  1.1.2 | 6| |
+|DenseNet-121| [32 MB](model/densenet-7.onnx)  |  [33 MB](model/densenet-7.tar.gz) |  1.2 | 7| |
+|DenseNet-121| [32 MB](model/densenet-8.onnx)  |  [33 MB](model/densenet-8.tar.gz) |  1.3 | 8| |
+|DenseNet-121| [32 MB](model/densenet-9.onnx)  |  [33 MB](model/densenet-9.tar.gz) |  1.4 | 9| |
+|DenseNet-121-12| [32 MB](model/densenet-12.onnx)  |  [30 MB](model/densenet-12.tar.gz) |  1.9 | 12| 60.96 |
+|DenseNet-121-12-int8| [9 MB](model/densenet-12-int8.onnx)  |  [6 MB](model/densenet-12-int8.tar.gz) |  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](https://arxiv.org/abs/1608.06993)
+
+### Dataset
+[ILSVRC2012](http://www.image-net.org/challenges/LSVRC/2012/)
+
+## 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](https://github.com/intel/neural-compressor) with onnxruntime backend to perform quantization. View the [instructions](https://github.com/intel/neural-compressor/blob/master/examples/onnxrt/image_recognition/onnx_model_zoo/densenet/quantization/ptq/README.md) to understand how to use Intel® Neural Compressor for quantization.
+
+### Environment
+onnx: 1.9.0
+onnxruntime: 1.10.0
+
+### Prepare model
+```shell
+wget https://github.com/onnx/models/raw/main/vision/classification/densenet-121/model/densenet-12.onnx
+```
+
+### Model quantize
+```bash
+bash run_tuning.sh --input_model=path/to/model \  # model path as *.onnx
+--config=densenet.yaml \
+--output_model=path/to/save
+```
+
+## References
+* [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
+
+## Contributors
+* [mengniwang95](https://github.com/mengniwang95) (Intel)
+* [airMeng](https://github.com/airMeng) (Intel)
+* [ftian1](https://github.com/ftian1) (Intel)
+* [hshen14](https://github.com/hshen14) (Intel)
+
+## License
+MIT
+