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R-CNN ILSVRC13

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Model size: 231 MB

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