---
language: en
license: apache-2.0
model_name: rcnn-ilsvrc13-8.onnx
tags:
- validated
- vision
- classification
- rcnn_ilsvrc13
---
<!--- SPDX-License-Identifier: BSD-3-Clause -->

# R-CNN ILSVRC13

|Model        |Download  |Download (with sample test data)| ONNX version |Opset version|
| ------------- | ------------- | ------------- | ------------- | ------------- |
|R-CNN ILSVRC13| [32 MB](model/rcnn-ilsvrc13-3.onnx)  |  [231 MB](model/rcnn-ilsvrc13-3.tar.gz) |  1.1 | 3|
|R-CNN ILSVRC13| [32 MB](model/rcnn-ilsvrc13-6.onnx)  |  [231 MB](model/rcnn-ilsvrc13-6.tar.gz) |  1.1.2 | 6|
|R-CNN ILSVRC13| [32 MB](model/rcnn-ilsvrc13-7.onnx)  |  [231 MB](model/rcnn-ilsvrc13-7.tar.gz) |  1.2 | 7|
|R-CNN ILSVRC13| [32 MB](model/rcnn-ilsvrc13-8.onnx)  |  [231 MB](model/rcnn-ilsvrc13-8.tar.gz) |  1.3 | 8|
|R-CNN ILSVRC13| [32 MB](model/rcnn-ilsvrc13-9.onnx)  |  [231 MB](model/rcnn-ilsvrc13-9.tar.gz) |  1.4 | 9|


## Description
R-CNN is a convolutional neural network for detection.
This model was made by transplanting the R-CNN SVM classifiers into a fc-rcnn classification layer.

### Paper
[Rich feature hierarchies for accurate object detection and semantic segmentation](https://arxiv.org/abs/1311.2524)

### Dataset
[ILSVRC2013](http://www.image-net.org/challenges/LSVRC/2013/)

## Source
Caffe BVLC R-CNN ILSVRC13 ==> Caffe2 R-CNN ILSVRC13 ==> ONNX R-CNN ILSVRC13

## Model input and output
### Input
```
data_0: float[1, 3, 224, 224]
```
### Output
```
fc-rcnn_1: float[1, 200]
```
### Pre-processing steps
### Post-processing steps
### Sample test data
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

## Results/accuracy on test set
On the 200-class ILSVRC2013 detection dataset, R-CNN’s mAP is 31.4%.

## License
[BSD-3](LICENSE)