Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,112 @@
+---
+language: en
+license: apache-2.0
+model_name: bvlcalexnet-3.onnx
+tags:
+- validated
+- vision
+- classification
+- alexnet
+---
+<!--- SPDX-License-Identifier: BSD-3-Clause -->
+
+# AlexNet
+
+|Model        |Download  |Download (with sample test data)| ONNX version |Opset version|Top-1 accuracy (%)|Top-5 accuracy (%)|
+| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
+|AlexNet| [238 MB](model/bvlcalexnet-3.onnx)  |  [225 MB](model/bvlcalexnet-3.tar.gz) |  1.1 | 3| | |
+|AlexNet| [238 MB](model/bvlcalexnet-6.onnx)  |  [225 MB](model/bvlcalexnet-6.tar.gz) |  1.1.2 | 6| | |
+|AlexNet| [238 MB](model/bvlcalexnet-7.onnx)  |  [226 MB](model/bvlcalexnet-7.tar.gz) |  1.2 | 7| | |
+|AlexNet| [238 MB](model/bvlcalexnet-8.onnx)  |  [226 MB](model/bvlcalexnet-8.tar.gz) |  1.3 | 8| | |
+|AlexNet| [238 MB](model/bvlcalexnet-9.onnx)  |  [226 MB](model/bvlcalexnet-9.tar.gz) |  1.4 | 9| | |
+|AlexNet| [233 MB](model/bvlcalexnet-12.onnx)  |  [216 MB](model/bvlcalexnet-12.tar.gz) |  1.9 | 12|54.80|78.23|
+|AlexNet-int8| [58 MB](model/bvlcalexnet-12-int8.onnx)  |  [39 MB](model/bvlcalexnet-12-int8.tar.gz) |  1.9 | 12|54.68|78.23|
+|AlexNet-qdq| [59 MB](model/bvlcalexnet-12-qdq.onnx)  |  [44 MB](model/bvlcalexnet-12-qdq.tar.gz) |  1.9 | 12|54.71|78.22|
+> 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.
+>
+> **Note**
+>
+> 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).
+>
+> 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
+AlexNet is the name of a convolutional neural network for classification,
+which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012.
+
+Differences:
+- not training with the relighting data-augmentation;
+- initializing non-zero biases to 0.1 instead of 1 (found necessary for training, as initialization to 1 gave flat loss).
+
+### Dataset
+[ILSVRC2012](http://www.image-net.org/challenges/LSVRC/2012/)
+
+## Source
+Caffe BVLC AlexNet ==> Caffe2 AlexNet ==> ONNX AlexNet
+
+## Model input and output
+### Input
+```
+data_0: float[1, 3, 224, 224]
+```
+### Output
+```
+softmaxout_1: float[1, 1000]
+```
+### Pre-processing steps
+### Post-processing steps
+### Sample test data
+Randomly generated sample 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
+The bundled model is the iteration 360,000 snapshot.
+The best validation performance during training was iteration
+358,000 with validation accuracy 57.258% and loss 1.83948.
+This model obtains a top-1 accuracy 57.1% and a top-5 accuracy
+80.2% on the validation set, using just the center crop.
+(Using the average of 10 crops, (4 + 1 center) * 2 mirror,
+should obtain a bit higher accuracy.)
+
+## Quantization
+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.
+
+### Environment
+onnx: 1.9.0
+onnxruntime: 1.8.0
+
+### Prepare model
+```shell
+wget https://github.com/onnx/models/raw/main/vision/classification/alexnet/model/bvlcalexnet-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=alexnet.yaml \
+--data_path=/path/to/imagenet \
+--label_path=/path/to/imagenet/label \
+--output_model=path/to/save
+```
+
+## References
+* [ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
+
+* [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
+[BSD-3](LICENSE)
+