| ---
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| language: en
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| license: apache-2.0
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| model_name: caffenet-7.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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| - caffenet
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| ---
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| <!--- SPDX-License-Identifier: BSD-3-Clause -->
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|
|
| # CaffeNet
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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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| |CaffeNet| [238 MB](model/caffenet-3.onnx) | [244 MB](model/caffenet-3.tar.gz) | 1.1 | 3| | |
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| |CaffeNet| [238 MB](model/caffenet-6.onnx) | [244 MB](model/caffenet-6.tar.gz) | 1.1.2 | 6| | |
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| |CaffeNet| [238 MB](model/caffenet-7.onnx) | [244 MB](model/caffenet-7.tar.gz) | 1.2 | 7| | |
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| |CaffeNet| [238 MB](model/caffenet-8.onnx) | [244 MB](model/caffenet-8.tar.gz) | 1.3 | 8| | |
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| |CaffeNet| [238 MB](model/caffenet-9.onnx) | [244 MB](model/caffenet-9.tar.gz) | 1.4 | 9| | |
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| |CaffeNet| [233 MB](model/caffenet-12.onnx) | [216 MB](model/caffenet-12.tar.gz) | 1.9 | 12|56.27 |79.52 |
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| |CaffeNet-int8| [58 MB](model/caffenet-12-int8.onnx) | [39 MB](model/caffenet-12-int8.tar.gz) | 1.9 | 12| 56.22|79.52 |
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| |CaffeNet-qdq| [59 MB](model/caffenet-12-qdq.onnx) | [44 MB](model/caffenet-12-qdq.tar.gz) | 1.9 | 12| 56.25|79.45 |
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| > Compared with the fp32 CaffeNet, int8 CaffeNet's Top-1 accuracy drop ratio is 0.09%, Top-5 accuracy drop ratio is 0.13% and performance improvement is 3.08x.
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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/caffenet/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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| CaffeNet a variant of AlexNet.
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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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| - the order of pooling and normalization layers is switched (in CaffeNet, pooling is done before normalization).
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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 CaffeNet ==> Caffe2 CaffeNet ==> ONNX CaffeNet
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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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| prob_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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| random generated sampe test data:
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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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| - test_data_set_3
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| - test_data_set_4
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| - test_data_set_5
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|
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| ## Results/accuracy on test set
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| This model is snapshot of iteration 310,000.
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| The best validation performance during training was iteration
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| 313,000 with validation accuracy 57.412% and loss 1.82328.
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| This model obtains a top-1 accuracy 57.4% and a top-5 accuracy
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| 80.4% 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 still.)
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|
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| ## Quantization
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| CaffeNet-int8 and CaffeNet-qdq are obtained by quantizing fp32 CaffeNet 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/caffenet/quantization/ptq/README.md) to understand how to use Intel® Neural Compressor for quantization.
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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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|
|
| ### Prepare model
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| ```shell
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| wget https://github.com/onnx/models/raw/main/vision/classification/caffenet/model/caffenet-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=caffenet.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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|
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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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|
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| ## License
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| [BSD-3](LICENSE)
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|
