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---
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language: en
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license: apache-2.0
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model_name: bvlcalexnet-3.onnx
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tags:
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- validated
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- vision
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- classification
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- alexnet
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---
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<!--- SPDX-License-Identifier: BSD-3-Clause -->
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# AlexNet
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|Model |Download |Download (with sample test data)| ONNX version |Opset version|Top-1 accuracy (%)|Top-5 accuracy (%)|
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| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
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|AlexNet| [238 MB](model/bvlcalexnet-3.onnx) | [225 MB](model/bvlcalexnet-3.tar.gz) | 1.1 | 3| | |
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|AlexNet| [238 MB](model/bvlcalexnet-6.onnx) | [225 MB](model/bvlcalexnet-6.tar.gz) | 1.1.2 | 6| | |
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|AlexNet| [238 MB](model/bvlcalexnet-7.onnx) | [226 MB](model/bvlcalexnet-7.tar.gz) | 1.2 | 7| | |
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|AlexNet| [238 MB](model/bvlcalexnet-8.onnx) | [226 MB](model/bvlcalexnet-8.tar.gz) | 1.3 | 8| | |
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|AlexNet| [238 MB](model/bvlcalexnet-9.onnx) | [226 MB](model/bvlcalexnet-9.tar.gz) | 1.4 | 9| | |
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|AlexNet| [233 MB](model/bvlcalexnet-12.onnx) | [216 MB](model/bvlcalexnet-12.tar.gz) | 1.9 | 12|54.80|78.23|
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|AlexNet-int8| [58 MB](model/bvlcalexnet-12-int8.onnx) | [39 MB](model/bvlcalexnet-12-int8.tar.gz) | 1.9 | 12|54.68|78.23|
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|AlexNet-qdq| [59 MB](model/bvlcalexnet-12-qdq.onnx) | [44 MB](model/bvlcalexnet-12-qdq.tar.gz) | 1.9 | 12|54.71|78.22|
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> Compared with the fp32 AlextNet, int8 AlextNet's Top-1 accuracy drop ratio is 0.22%, Top-5 accuracy drop ratio is 0.05% and performance improvement is 2.26x.
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>
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> **Note**
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>
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> Different preprocess methods will lead to different accuracies, the accuracy in table depends on this specific [preprocess method](https://github.com/intel/neural-compressor/blob/master/examples/onnxrt/image_recognition/onnx_model_zoo/alexnet/quantization/ptq/main.py).
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>
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> 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.
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## Description
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AlexNet is the name of a convolutional neural network for classification,
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which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012.
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Differences:
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- not training with the relighting data-augmentation;
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- initializing non-zero biases to 0.1 instead of 1 (found necessary for training, as initialization to 1 gave flat loss).
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### Dataset
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[ILSVRC2012](http://www.image-net.org/challenges/LSVRC/2012/)
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## Source
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Caffe BVLC AlexNet ==> Caffe2 AlexNet ==> ONNX AlexNet
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## Model input and output
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### Input
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```
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data_0: float[1, 3, 224, 224]
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```
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### Output
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```
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softmaxout_1: float[1, 1000]
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```
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### Pre-processing steps
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### Post-processing steps
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### Sample test data
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Randomly generated sample test data:
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- test_data_0.npz
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- test_data_1.npz
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- test_data_2.npz
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- test_data_set_0
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- test_data_set_1
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- test_data_set_2
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## Results/accuracy on test set
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The bundled model is the iteration 360,000 snapshot.
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The best validation performance during training was iteration
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358,000 with validation accuracy 57.258% and loss 1.83948.
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This model obtains a top-1 accuracy 57.1% and a top-5 accuracy
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80.2% on the validation set, using just the center crop.
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(Using the average of 10 crops, (4 + 1 center) * 2 mirror,
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should obtain a bit higher accuracy.)
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## Quantization
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AlexNet-int8 and AlexNet-qdq are obtained by quantizing fp32 AlexNet 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/alexnet/quantization/ptq/README.md) to understand how to use Intel® Neural Compressor for quantization.
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### Environment
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onnx: 1.9.0
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onnxruntime: 1.8.0
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### Prepare model
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```shell
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wget https://github.com/onnx/models/raw/main/vision/classification/alexnet/model/bvlcalexnet-12.onnx
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```
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### Model quantize
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Make sure to specify the appropriate dataset path in the configuration file.
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```bash
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bash run_tuning.sh --input_model=path/to/model \ # model path as *.onnx
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--config=alexnet.yaml \
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--data_path=/path/to/imagenet \
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--label_path=/path/to/imagenet/label \
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--output_model=path/to/save
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```
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## References
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* [ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
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* [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
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## Contributors
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* [mengniwang95](https://github.com/mengniwang95) (Intel)
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* [yuwenzho](https://github.com/yuwenzho) (Intel)
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* [airMeng](https://github.com/airMeng) (Intel)
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* [ftian1](https://github.com/ftian1) (Intel)
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* [hshen14](https://github.com/hshen14) (Intel)
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## License
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[BSD-3](LICENSE)
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