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README.md

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+---
+language: en
+license: apache-2.0
+model_name: zfnet512-3.onnx
+tags:
+- validated
+- vision
+- classification
+- zfnet-512
+---
+<!--- SPDX-License-Identifier: MIT -->
+
+# ZFNet-512
+
+|Model        |Download  |Download (with sample test data)| ONNX version |Opset version|Top-1 accuracy (%)|Top-5 accuracy (%)|
+| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
+|ZFNet-512| [341 MB](model/zfnet512-3.onnx)  |  [320 MB](model/zfnet512-3.tar.gz) |  1.1 | 3| | |
+|ZFNet-512| [341 MB](model/zfnet512-6.onnx)  |  [320 MB](model/zfnet512-6.tar.gz) |  1.1.2 | 6| | |
+|ZFNet-512| [341 MB](model/zfnet512-7.onnx)  |  [320 MB](model/zfnet512-7.tar.gz) |  1.2 | 7| | |
+|ZFNet-512| [341 MB](model/zfnet512-8.onnx)  |  [318 MB](model/zfnet512-8.tar.gz) |  1.3 | 8| | |
+|ZFNet-512| [341 MB](model/zfnet512-9.onnx)  |  [318 MB](model/zfnet512-9.tar.gz) |  1.4 | 9| | |
+|ZFNet-512| [333 MB](model/zfnet512-12.onnx)  |  [309 MB](model/zfnet512-12.tar.gz) |  1.9 | 12|55.97|79.41|
+|ZFNet-512-int8| [83 MB](model/zfnet512-12-int8.onnx)  |  [48 MB](model/zfnet512-12-int8.tar.gz) |  1.9 | 12|55.84|79.33|
+|ZFNet-512-qdq| [84 MB](model/zfnet512-12-qdq.onnx)  |  [56 MB](model/zfnet512-12-qdq.tar.gz) |  1.9 | 12|55.83|79.42|
+> Compared with the fp32 ZFNet-512, int8 ZFNet-512's Top-1 accuracy drop ratio is 0.23%, Top-5 accuracy drop ratio is 0.10% and performance improvement is 1.78x.
+>
+> **Note**
+>
+> Different preprocess methods will lead to different accuracies, the accuracy in table depends on this specific [preprocess method](https://github.com/intel-innersource/frameworks.ai.lpot.intel-lpot/blob/master/examples/onnxrt/onnx_model_zoo/zfnet/main.py).
+>
+> 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
+ZFNet-512 is a deep convolutional networks for classification.
+This model's 4th layer has 512 maps instead of 1024 maps mentioned in the paper.
+
+### Dataset
+[ILSVRC2013](http://www.image-net.org/challenges/LSVRC/2013/)
+
+## Source
+Caffe2 ZFNet-512 ==> ONNX ZFNet-512
+
+## Model input and output
+### Input
+```
+gpu_0/data_0: float[1, 3, 224, 224]
+```
+### Output
+```
+gpu_0/softmax_1: float[1, 1000]
+```
+### Pre-processing steps
+### Post-processing steps
+### Sample test data
+random generated sampe test data:
+- test_data_set_0
+- test_data_set_1
+- test_data_set_2
+- test_data_set_3
+- test_data_set_4
+- test_data_set_5
+
+## Results/accuracy on test set
+
+## Quantization
+ZFNet-512-int8 and ZFNet-512-qdq are obtained by quantizing fp32 ZFNet-512 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/zfnet/quantization/ptq/README.md) to understand how to use Intel® Neural Compressor for quantization.
+
+### Environment
+onnx: 1.9.0
+onnxruntime: 1.8.0
+
+### Prepare model
+```shell
+wget https://github.com/onnx/models/raw/main/vision/classification/zfnet-512/model/zfnet512-12.onnx
+```
+
+### Model quantize
+Make sure to specify the appropriate dataset path in the configuration file.
+```bash
+bash run_tuning.sh --input_model=path/to/model \  # model path as *.onnx
+--config=zfnet512.yaml \
+--data_path=/path/to/imagenet \
+--label_path=/path/to/imagenet/label \
+--output_model=path/to/save
+```
+
+## References
+* [Visualizing and Understanding Convolutional Networks](https://arxiv.org/abs/1311.2901)
+
+* [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
+
+## Contributors
+* [mengniwang95](https://github.com/mengniwang95) (Intel)
+* [yuwenzho](https://github.com/yuwenzho) (Intel)
+* [airMeng](https://github.com/airMeng) (Intel)
+* [ftian1](https://github.com/ftian1) (Intel)
+* [hshen14](https://github.com/hshen14) (Intel)
+
+## License
+MIT
+