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rcnn-ilsvrc13-7
onnxmodelzoo/rcnn-ilsvrc13-7
rcnn-ilsvrc13-7 is a machine learning model from onnxmodelzoo. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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.
Access
Public
Updated Sep 30, 2025
Repo size
231 MB
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Repo makeup
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Sep 30, 2025
- Updated
- Sep 30, 2025
- SHA
- cb3b15eb
Model card
From the Hugging Face model README
<!--- 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 | 231 MB | 1.1 | 3 |
| R-CNN ILSVRC13 | 32 MB | 231 MB | 1.1.2 | 6 |
| R-CNN ILSVRC13 | 32 MB | 231 MB | 1.2 | 7 |
| R-CNN ILSVRC13 | 32 MB | 231 MB | 1.3 | 8 |
| R-CNN ILSVRC13 | 32 MB | 231 MB | 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
Dataset
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
- License
- apache-2.0
- Languages
- en
- Created
- Sep 30, 2025
- Updated
- Sep 30, 2025