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---
language: en
license: apache-2.0
model_name: inception-v2-7.onnx
tags:
- validated
- vision
- classification
- inception_and_googlenet
- inception_v2
---
<!--- SPDX-License-Identifier: MIT -->
# Inception v2
|Model |Download |Download (with sample test data)| ONNX version |Opset version|
| ------------- | ------------- | ------------- | ------------- | ------------- |
|Inception-2| [44 MB](model/inception-v2-3.onnx) | [44 MB](model/inception-v2-3.tar.gz) | 1.1 | 3|
|Inception-2| [44 MB](model/inception-v2-6.onnx) | [44 MB](model/inception-v2-6.tar.gz) | 1.1.2 | 6|
|Inception-2| [44 MB](model/inception-v2-7.onnx) | [44 MB](model/inception-v2-7.tar.gz) | 1.2 | 7|
|Inception-2| [44 MB](model/inception-v2-8.onnx) | [44 MB](model/inception-v2-8.tar.gz) | 1.3 | 8|
|Inception-2| [44 MB](model/inception-v2-9.onnx) | [44 MB](model/inception-v2-9.tar.gz) | 1.4 | 9|
## Description
Inception v2 is a deep convolutional networks for classification.
### Paper
[Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567)
### Dataset
[ILSVRC2012](http://www.image-net.org/challenges/LSVRC/2012/)
## Source
Caffe2 Inception v2 ==> ONNX Inception v2
## Model input and output
### Input
```
data_0: float[1, 3, 224, 224]
```
### Output
```
prob_1: float[1, 1000]
```
### Pre-processing steps
### Post-processing steps
### Sample test data
random generated sampe test data:
- test_data_0.npz
- test_data_1.npz
- test_data_2.npz
- test_data_set_0
- test_data_set_1
- test_data_set_2
## Results/accuracy on test set
## License
MIT
|