| | ---
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| | language: en
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| | license: apache-2.0
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| | model_name: bvlcalexnet-3.onnx
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| | tags:
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| | - validated
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| | - vision
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| | - classification
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| | - alexnet
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| | ---
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| | <!--- SPDX-License-Identifier: BSD-3-Clause -->
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| |
|
| | # AlexNet
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| |
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| | |Model |Download |Download (with sample test data)| ONNX version |Opset version|Top-1 accuracy (%)|Top-5 accuracy (%)|
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| | | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
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| | |AlexNet| [238 MB](model/bvlcalexnet-3.onnx) | [225 MB](model/bvlcalexnet-3.tar.gz) | 1.1 | 3| | |
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| | |AlexNet| [238 MB](model/bvlcalexnet-6.onnx) | [225 MB](model/bvlcalexnet-6.tar.gz) | 1.1.2 | 6| | |
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| | |AlexNet| [238 MB](model/bvlcalexnet-7.onnx) | [226 MB](model/bvlcalexnet-7.tar.gz) | 1.2 | 7| | |
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| | |AlexNet| [238 MB](model/bvlcalexnet-8.onnx) | [226 MB](model/bvlcalexnet-8.tar.gz) | 1.3 | 8| | |
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| | |AlexNet| [238 MB](model/bvlcalexnet-9.onnx) | [226 MB](model/bvlcalexnet-9.tar.gz) | 1.4 | 9| | |
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| | |AlexNet| [233 MB](model/bvlcalexnet-12.onnx) | [216 MB](model/bvlcalexnet-12.tar.gz) | 1.9 | 12|54.80|78.23|
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| | |AlexNet-int8| [58 MB](model/bvlcalexnet-12-int8.onnx) | [39 MB](model/bvlcalexnet-12-int8.tar.gz) | 1.9 | 12|54.68|78.23|
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| | |AlexNet-qdq| [59 MB](model/bvlcalexnet-12-qdq.onnx) | [44 MB](model/bvlcalexnet-12-qdq.tar.gz) | 1.9 | 12|54.71|78.22|
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| | > 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.
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| | >
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| | > **Note**
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| | >
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| | > 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).
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| | >
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| | > 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.
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| |
|
| | ## Description
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| | AlexNet is the name of a convolutional neural network for classification,
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| | which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012.
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| |
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| | Differences:
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| | - not training with the relighting data-augmentation;
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| | - initializing non-zero biases to 0.1 instead of 1 (found necessary for training, as initialization to 1 gave flat loss).
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| |
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| | ### Dataset
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| | [ILSVRC2012](http://www.image-net.org/challenges/LSVRC/2012/)
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| |
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| | ## Source
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| | Caffe BVLC AlexNet ==> Caffe2 AlexNet ==> ONNX AlexNet
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| |
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| | ## Model input and output
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| | ### Input
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| | ```
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| | data_0: float[1, 3, 224, 224]
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| | ```
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| | ### Output
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| | ```
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| | softmaxout_1: float[1, 1000]
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| | ```
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| | ### Pre-processing steps
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| | ### Post-processing steps
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| | ### Sample test data
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| | Randomly generated sample test data:
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| | - test_data_0.npz
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| | - test_data_1.npz
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| | - test_data_2.npz
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| | - test_data_set_0
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| | - test_data_set_1
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| | - test_data_set_2
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| |
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| | ## Results/accuracy on test set
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| | The bundled model is the iteration 360,000 snapshot.
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| | The best validation performance during training was iteration
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| | 358,000 with validation accuracy 57.258% and loss 1.83948.
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| | This model obtains a top-1 accuracy 57.1% and a top-5 accuracy
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| | 80.2% on the validation set, using just the center crop.
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| | (Using the average of 10 crops, (4 + 1 center) * 2 mirror,
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| | should obtain a bit higher accuracy.)
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| |
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| | ## Quantization
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| | 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.
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| |
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| | ### Environment
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| | onnx: 1.9.0
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| | onnxruntime: 1.8.0
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| |
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| | ### Prepare model
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| | ```shell
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| | wget https://github.com/onnx/models/raw/main/vision/classification/alexnet/model/bvlcalexnet-12.onnx
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| | ```
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| |
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| | ### Model quantize
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| | Make sure to specify the appropriate dataset path in the configuration file.
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| | ```bash
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| | bash run_tuning.sh --input_model=path/to/model \ # model path as *.onnx
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| | --config=alexnet.yaml \
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| | --data_path=/path/to/imagenet \
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| | --label_path=/path/to/imagenet/label \
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| | --output_model=path/to/save
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| | ```
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| |
|
| | ## References
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| | * [ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
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| |
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| | * [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
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| |
|
| | ## Contributors
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| | * [mengniwang95](https://github.com/mengniwang95) (Intel)
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| | * [yuwenzho](https://github.com/yuwenzho) (Intel)
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| | * [airMeng](https://github.com/airMeng) (Intel)
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| | * [ftian1](https://github.com/ftian1) (Intel)
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| | * [hshen14](https://github.com/hshen14) (Intel)
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| |
|
| | ## License
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| | [BSD-3](LICENSE)
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| |
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| | |
