GoogleNet
| Model | Download | Download (with sample test data) | ONNX version | Opset version | Top-1 accuracy (%) | Top-5 accuracy (%) |
|---|---|---|---|---|---|---|
| GoogleNet | 28 MB | 31 MB | 1.1 | 3 | ||
| GoogleNet | 28 MB | 31 MB | 1.1.2 | 6 | ||
| GoogleNet | 28 MB | 31 MB | 1.2 | 7 | ||
| GoogleNet | 28 MB | 31 MB | 1.3 | 8 | ||
| GoogleNet | 28 MB | 31 MB | 1.4 | 9 | ||
| GoogleNet | 27 MB | 25 MB | 1.9 | 12 | 67.78 | 88.34 |
| GoogleNet-int8 | 7 MB | 5 MB | 1.9 | 12 | 67.73 | 88.32 |
| GoogleNet-qdq | 7 MB | 5 MB | 1.12 | 12 | 67.73 | 88.31 |
Compared with the fp32 GoogleNet, int8 GoogleNet's Top-1 accuracy drop ratio is 0.07%, Top-5 accuracy drop ratio is 0.02% and performance improvement is 1.27x.
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.
Description
GoogLeNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2014.
Differences:
- not training with the relighting data-augmentation;
- not training with the scale or aspect-ratio data-augmentation;
- uses "xavier" to initialize the weights instead of "gaussian";
Dataset
Source
Caffe BVLC GoogLeNet ==> Caffe2 GoogLeNet ==> ONNX GoogLeNet
Model input and output
Input
data_0: float[1, 3, 224, 224]
Output
prob_0: float[1, 1000]
Pre-processing steps
Necessary Imports
import imageio
from PIL import Image
Obtain and pre-process image
def get_image(path):
'''
Using path to image, return the RGB load image
'''
img = imageio.imread(path, pilmode='RGB')
return img
# Pre-processing function for ImageNet models using numpy
def preprocess(img):
'''
Preprocessing required on the images for inference with mxnet gluon
The function takes loaded image and returns processed tensor
'''
img = np.array(Image.fromarray(img).resize((224, 224))).astype(np.float32)
img[:, :, 0] -= 123.68
img[:, :, 1] -= 116.779
img[:, :, 2] -= 103.939
img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, axis=0)
return img
Post-processing steps
def predict(path):
# based on : https://mxnet.apache.org/versions/1.0.0/tutorials/python/predict_image.html
img = get_image(path)
img = preprocess(img)
mod.forward(Batch([mx.nd.array(img)]))
# Take softmax to generate probabilities
prob = mod.get_outputs()[0].asnumpy()
prob = np.squeeze(prob)
a = np.argsort(prob)[::-1]
return a
Sample test data
random generated sample 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
This bundled model obtains a top-1 accuracy 68.7% (31.3% error) and a top-5 accuracy 88.9% (11.1% error) 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
GoogleNet-int8 and GoogleNet-qdq are obtained by quantizing fp32 GoogleNet model. We use Intel® Neural Compressor with onnxruntime backend to perform quantization. View the instructions to understand how to use Intel® Neural Compressor for quantization.
Environment
onnx: 1.9.0 onnxruntime: 1.8.0
Prepare model
wget https://github.com/onnx/models/raw/main/vision/classification/inception_and_googlenet/googlenet/model/googlenet-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=googlenet.yaml \
--data_path=/path/to/imagenet \
--label_path=/path/to/imagenet/label \
--output_model=path/to/save
References
Contributors
- mengniwang95 (Intel)
- airMeng (Intel)
- ftian1 (Intel)
- hshen14 (Intel)