Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,134 @@
+---
+language: en
+license: apache-2.0
+model_name: shufflenet-8.onnx
+tags:
+- validated
+- vision
+- classification
+- shufflenet
+---
+<!--- SPDX-License-Identifier: BSD-3-Clause -->
+
+# ShuffleNet
+
+## Use cases
+Computationally efficient CNN architecture designed specifically for mobile devices with very limited computing power.
+
+## Description
+ShuffleNet is a deep convolutional network for image classification. [ShuffleNetV2](https://pytorch.org/hub/pytorch_vision_shufflenet_v2/) is an improved architecture that is the state-of-the-art in terms of speed and accuracy tradeoff used for image classification.
+
+Caffe2 ShuffleNet-v1 ==> ONNX ShuffleNet-v1
+
+PyTorch ShuffleNet-v2 ==> ONNX ShuffleNet-v2
+
+ONNX ShuffleNet-v2 ==> Quantized ONNX ShuffleNet-v2
+
+ONNX ShuffleNet-v2 ==> Quantized ONNX ShuffleNet-v2
+
+## Model
+
+|Model        |Download  |Download (with sample test data)|ONNX version|Opset version|
+|-------------|:--------------|:--------------|:--------------|:--------------|
+|ShuffleNet-v1| [5.3 MB](model/shufflenet-3.onnx)  |  [7 MB](model/shufflenet-3.tar.gz) |  1.1 | 3|
+|ShuffleNet-v1| [5.3 MB](model/shufflenet-6.onnx)  |  [9 MB](model/shufflenet-6.tar.gz) |  1.1.2 | 6|
+|ShuffleNet-v1| [5.3 MB](model/shufflenet-7.onnx)  |  [9 MB](model/shufflenet-7.tar.gz) |  1.2 | 7|
+|ShuffleNet-v1| [5.3 MB](model/shufflenet-8.onnx)  |  [9 MB](model/shufflenet-8.tar.gz) |  1.3 | 8|
+|ShuffleNet-v1| [5.3 MB](model/shufflenet-9.onnx)  |  [9 MB](model/shufflenet-9.tar.gz) |  1.4 | 9|
+
+|Model        |Download  |Download (with sample test data)|ONNX version|Opset version|Top-1 error |Top-5 error |
+|-------------|:--------------|:--------------|:--------------|:--------------|:--------------|:--------------|
+|ShuffleNet-v2 |[9.2MB](model/shufflenet-v2-10.onnx) |  [8.7MB](model/shufflenet-v2-10.tar.gz) | 1.6 | 10 | 30.64 | 11.68|
+|ShuffleNet-v2-fp32 |[8.79MB](model/shufflenet-v2-12.onnx) |[8.69MB](model/shufflenet-v2-12.tar.gz) |1.9 |12 |33.65 |13.43|
+|ShuffleNet-v2-int8 |[2.28MB](model/shufflenet-v2-12-int8.onnx) |[2.37MB](model/shufflenet-v2-12-int8.tar.gz) |1.9 |12 |33.85 |13.66 |
+|ShuffleNet-v2-qdq |[2.30MB](model/shufflenet-v2-12-qdq.onnx) |[2.68MB](model/shufflenet-v2-12-qdq.tar.gz) |1.12 |12 |33.88 | 19.94 |
+> Compared with the fp32 ShuffleNet-v2, int8 ShuffleNet-v2's Top-1 error rising ratio is 0.59%, Top-5 error rising ratio is 1.71% and performance improvement is 1.62x.
+>
+> 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.
+
+## Inference
+[This script](ShufflenetV2-export.py) converts the ShuffleNetv2 model from PyTorch to ONNX and uses ONNX Runtime for inference.
+
+### Input to model
+Input to the model are 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224.
+```
+data_0: float[1, 3, 224, 224]
+```
+
+### Preprocessing steps
+All pre-trained models expect input images normalized in the same way. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].
+
+```python
+input_image = Image.open(filename)
+preprocess = transforms.Compose([
+transforms.Resize(256),
+transforms.CenterCrop(224),
+transforms.ToTensor(),
+transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+input_tensor = preprocess(input_image)
+```
+Create a mini-batch as expected by the model.
+```python
+input_batch = input_tensor.unsqueeze(0)
+```
+
+### Output of model
+
+Output of this model is tensor of shape 1000, with confidence scores over ImageNet's 1000 classes.
+```
+softmax_1: float[1, 1000]
+```
+<hr>
+
+## Dataset (Train and Validation)
+Models are pretrained on ImageNet.
+For training we use train+valset in COCO except for 5000 images from minivalset, and use the minivalset to test.
+Details of performance on COCO object detection are provided in [this paper](https://arxiv.org/pdf/1807.11164v1.pdf)
+<hr>
+
+## Quantization
+ShuffleNet-v2-int8 and ShuffleNet-v2-int8 are obtained by quantizing ShuffleNet-v2-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/shufflenet/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/shufflenet/model/shufflenet-v2-12.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=shufflenetv2.yaml \
+--output_model=path/to/save
+```
+
+### Model inference
+We use onnxruntime to perform ShuffleNetv2_fp32 and ShuffleNetv2_int8 inference. View the notebook [onnxrt_inference](../onnxrt_inference.ipynb) to understand how to use these 2 models for doing inference as well as which preprocess and postprocess we use.
+
+## References
+* Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun. ShuffleNet V2: Practical Guidelines for EfficientCNN Architecture Design. 2018.
+
+* huffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://arxiv.org/abs/1707.01083)
+
+* [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
+<hr>
+
+## Contributors
+* Ksenija Stanojevic
+* [mengniwang95](https://github.com/mengniwang95) (Intel)
+* [airMeng](https://github.com/airMeng) (Intel)
+* [ftian1](https://github.com/ftian1) (Intel)
+* [hshen14](https://github.com/hshen14) (Intel)
+<hr>
+
+## License
+BSD 3-Clause License
+<hr>
+