Model | Download | Download (with sample test data) | ONNX version | Opset version | Top-1 accuracy (%) | Top-5 accuracy (%) |
---|---|---|---|---|---|---|
ZFNet-512 | 341 MB | 320 MB | 1.1 | 3 | ||
ZFNet-512 | 341 MB | 320 MB | 1.1.2 | 6 | ||
ZFNet-512 | 341 MB | 320 MB | 1.2 | 7 | ||
ZFNet-512 | 341 MB | 318 MB | 1.3 | 8 | ||
ZFNet-512 | 341 MB | 318 MB | 1.4 | 9 | ||
ZFNet-512 | 333 MB | 309 MB | 1.9 | 12 | 55.97 | 79.41 |
ZFNet-512-int8 | 83 MB | 48 MB | 1.9 | 12 | 55.84 | 79.33 |
Compared with the fp32 ZFNet-512, int8 ZFNet-512's Top-1 accuracy drop ratio is 0.23%, Top-5 accuracy drop ratio is 0.10% and performance improvement is 1.78x.
Note
Different preprocess methods will lead to different accuracies, the accuracy in table depends on this specific preprocess method.
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.
ZFNet-512 is a deep convolutional networks for classification. This model's 4th layer has 512 maps instead of 1024 maps mentioned in the paper.
Caffe2 ZFNet-512 ==> ONNX ZFNet-512
gpu_0/data_0: float[1, 3, 224, 224]
gpu_0/softmax_1: float[1, 1000]
random generated sampe 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
ZFNet-512-int8 is obtained by quantizing fp32 ZFNet-512 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.
onnx: 1.9.0 onnxruntime: 1.8.0
wget https://github.com/onnx/models/blob/master/vision/classification/zfnet-512/model/zfnet512-12.onnx
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=zfnet512.yaml \
--data_path=/path/to/imagenet \
--label_path=/path/to/imagenet/label \
--output_model=path/to/save
- mengniwang95 (Intel)
- airMeng (Intel)
- ftian1 (Intel)
- hshen14 (Intel)
MIT