| # How to use hybrid-quantization function | |
| ## Model Source | |
| The model used in this example come from: | |
| https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md ssd_mobilenet_v2_coco | |
| ## Script Usage | |
| *Usage:* | |
| ``` | |
| 1. python step1.py | |
| 2. modify ssd_mobilenet_v2.quantization.cfg according to the prompt of step1.py | |
| 3. python step2.py | |
| ``` | |
| *Description:* | |
| - The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. | |
| - If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. | |
| ## Expected Results | |
| This example will outputs the results of the accuracy analysis and save the result of object detection to the 'result.jpg', as follows: | |
| ``` | |
| layer_name simulator_error | |
| entire single | |
| cos euc cos euc | |
| ----------------------------------------------------------------------------------------------------------------------------------------------------- | |
| [Input] FeatureExtractor/MobilenetV2/MobilenetV2/input:0 1.00000 | 0.0 1.00000 | 0.0 | |
| [exDataConvert] FeatureExtractor/MobilenetV2/MobilenetV2/input:0_int8 0.99996 | 2.0377 0.99996 | 2.0377 | |
| [Conv] Conv__350:0 | |
| [Clip] FeatureExtractor/MobilenetV2/Conv/Relu6:0 0.99998 | 9.5952 0.99998 | 9.5952 | |
| [Conv] FeatureExtractor/MobilenetV2/expanded_conv/depthwise/BatchNorm/batchnorm/add_1:0 | |
| [Clip] FeatureExtractor/MobilenetV2/expanded_conv/depthwise/Relu6:0 0.99951 | 65.269 0.99957 | 61.673 | |
| .... | |
| [Concat] concat:0_before_conv 0.99817 | 9.3381 1.00000 | 0.0317 | |
| [exDataConvert] concat:0_before_conv__int8 0.99812 | 9.4634 0.99994 | 1.6116 | |
| [Conv] concat:0_int8 0.99812 | 9.4634 0.99994 | 1.6115 | |
| [exDataConvert] concat:0 0.99812 | 9.4634 0.99994 | 1.6115 | |
| ``` | |
|  | |
| - Note: Different platforms, different versions of tools and drivers may have slightly different results. |