rcnn_ilsvrc13

R-CNN ILSVRC13

Download:

• release 1.1: https://s3.amazonaws.com/download.onnx/models/opset_3/bvlc_reference_rcnn_ilsvrc13.tar.gz
• release 1.1.2: https://s3.amazonaws.com/download.onnx/models/opset_6/bvlc_reference_rcnn_ilsvrc13.tar.gz
• release 1.2: https://s3.amazonaws.com/download.onnx/models/opset_7/bvlc_reference_rcnn_ilsvrc13.tar.gz
• release 1.3: https://s3.amazonaws.com/download.onnx/models/opset_8/bvlc_reference_rcnn_ilsvrc13.tar.gz
• master: https://s3.amazonaws.com/download.onnx/models/opset_9/bvlc_reference_rcnn_ilsvrc13.tar.gz

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