rcnn_ilsvrc13

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

ILSVRC2013

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