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README.md

ResNet

Use cases

ResNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. ResNet models provide very high accuracies with affordable model sizes. They are ideal for cases when high accuracy of classification is required.

Description

Deeper neural networks are more difficult to train. Residual learning framework ease the training of networks that are substantially deeper. The research explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. It also provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset the residual nets were evaluated with a depth of up to 152 layers — 8× deeper than VGG nets but still having lower complexity.

MXNet ResNet-v1 ==> ONNX ResNet-v1 [18, 34, 50, 101, 152]

MXNet ResNet-v2 ==> ONNX ResNet-v2 [18, 34, 50, 101, 152]

Caffe2 ResNet-50 ==> ONNX ResNet [50-caffe2]

ONNX ResNet50-v1 ==> Quantized ONNX ResNet50-v1

Model

The model below are ResNet v1 and v2. ResNet models consists of residual blocks and came up to counter the effect of deteriorating accuracies with more layers due to network not learning the initial layers. ResNet v2 uses pre-activation function whereas ResNet v1 uses post-activation for the residual blocks. The models below have 18, 34, 50, 101 and 152 layers for with ResNetv1 and ResNetv2 architecture.

Run ResNet-50 in browser - implemented by ONNX.js with ResNet50-Caffe2 release 1.2

  • ResNet V1:
Model Download Download (with sample test data) ONNX version Opset version Top-1 accuracy (%) Top-5 accuracy (%)
ResNet18 44.7 MB 42.9 MB 1.2.1 7 69.93 89.29
ResNet34 83.3 MB 78.6 MB 1.2.1 7 73.73 91.40
ResNet50 97.8 MB 92.2 MB 1.2.1 7 74.93 92.38
ResNet101 170.6 MB 159.8 MB 1.2.1 7 76.48 93.20
ResNet152 230.6 MB 217.2 MB 1.2.1 7 77.11 93.61
ResNet50_fp32 97.8 MB 92.0 MB 1.7.0 12 74.97 92.33
ResNet50_int8 24.6 MB 22.3 MB 1.7.0 12 74.77 92.32

Compared with the fp32 ResNet50, int8 ResNet50's Top-1 accuracy drop ratio is 0.27%, Top-5 accuracy drop ratio is 0.01% and performance improvement is 1.82x.

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.

Model Download Download (with sample test data) ONNX version Opset version
ResNet50-caffe2 32 MB 95 MB 1.1 3
ResNet50-caffe2 32 MB 96 MB 1.1.2 6
ResNet50-caffe2 32 MB 100 MB 1.2 7
ResNet50-caffe2 32 MB 100 MB 1.3 8
ResNet50-caffe2 32 MB 100 MB 1.4 9
  • ResNet V2:
Model Download Download (with sample test data) ONNX version Opset version Top-1 accuracy (%) Top-5 accuracy (%)
ResNet18 44.6 MB 42.9 MB 1.2.1 7 69.70 89.49
ResNet34 83.2 MB 78.6 MB 1.2.1 7 73.36 91.43
ResNet50 97.7 MB 92.0 MB 1.2.1 7 75.81 92.82
ResNet101 170.4 MB 159.4 MB 1.2.1 7 77.42 93.61
ResNet152 230.3 MB 216.0 MB 1.2.1 7 78.20 94.21

Inference

We used MXNet as framework with gluon APIs to perform inference. View the notebook imagenet_inference to understand how to use above models for doing inference. Make sure to specify the appropriate model name in the notebook.

Input

All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), where N is the batch size, and H and W are expected to be at least 224. The inference was done using jpeg image.

Preprocessing

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]. The transformation should preferably happen at preprocessing.

The following code shows how to preprocess a NCHW tensor:

import numpy

def preprocess(img_data):
    mean_vec = np.array([0.485, 0.456, 0.406])
    stddev_vec = np.array([0.229, 0.224, 0.225])
    norm_img_data = np.zeros(img_data.shape).astype('float32')
    for i in range(img_data.shape[0]):
         # for each pixel in each channel, divide the value by 255 to get value between [0, 1] and then normalize
        norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]
    return norm_img_data

Check imagenet_preprocess.py for additional sample code.

Output

The model outputs image scores for each of the 1000 classes of ImageNet.

Postprocessing

The post-processing involves calculating the softmax probability scores for each class. You can also sort them to report the most probable classes. Check imagenet_postprocess.py for code.

To do quick inference with the model, check out Model Server.

Dataset

Dataset used for train and validation: ImageNet (ILSVRC2012). Check imagenet_prep for guidelines on preparing the dataset.

Caffe2 Version of ResNet50 uses the ImageNet dataset from 2015 -- ILSVRC2015.

Validation accuracy

The accuracies obtained by the models on the validation set are mentioned above. The validation was done using center cropping of images unlike the paper which uses ten-cropping. We expect an increase of 1-2% in accuracies using ten cropping and that would lead to accuracies similar to the paper.

Training

We used MXNet as framework with gluon APIs to perform training. View the training notebook to understand details for parameters and network for each of the above variants of ResNet.

Validation

We used MXNet as framework with gluon APIs to perform validation. Use the notebook imagenet_validation to verify the accuracy of the model on the validation set. Make sure to specify the appropriate model name in the notebook.

Quantization

ResNet50_int8 is obtained by quantizing ResNet50_fp32 model. We use Intel® Low Precision Optimization Tool (LPOT) with onnxruntime backend to perform quantization. View the instructions to understand how to use LPOT for quantization.

Environment

onnx: 1.7.0 onnxruntime: 1.6.0+

Prepare model

wget https://github.com/onnx/models/tree/master/vision/classification/resnet/model/resnet50-v1-12.onnx

Model quantize

Make sure to specify the appropriate dataset path in the configuration file.

bash run_tuning.sh --input_model=path/to/model \  # model path as *.onnx
                   --config=resnet50_v1_5.yaml \
                   --output_model=path/to/save

Model inference

We use onnxruntime to perform Resnet50_fp32 and Resnet50_int8 inference. View the notebook onnxrt_inference to understand how to use these 2 models for doing inference as well as which preprocess and postprocess we use.

References

Contributors

License

Apache 2.0