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# How to use accuracy-analysis function
## Model Source
The model used in this example come from:
https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx
## Script Usage
*Usage:*
```
python test.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.accuracy_analysis'.
## Expected Results
This example will outputs the results of the accuracy analysis and store all the results in the snapshot directory, as follows:
```
# simulator_error: calculate the output error of each layer of the simulator (compared to the 'golden' value).
# entire: output error of each layer between 'golden' and 'simulator', these errors will accumulate layer by layer.
# single: single-layer output error between 'golden' and 'simulator', can better reflect the single-layer accuracy of the simulator.
layer_name simulator_error
entire single
cos euc cos euc
--------------------------------------------------------------------------------------------------
[Input] data 1.00000 | 0.0 1.00000 | 0.0
[exDataConvert] data_int8 0.99997 | 2.3275 0.99997 | 2.3275
[BatchNormalization] resnetv24_batchnorm0_fwd 0.99995 | 2.4514 0.99995 | 2.4514
...
[Relu] resnetv24_relu1_fwd 0.98352 | 41.498 0.99989 | 3.3598
[Conv] resnetv24_pool1_fwd 0.99545 | 2.2519 0.99999 | 0.1243
[Conv] resnetv24_dense0_fwd_conv 0.99450 | 6.8046 0.99993 | 0.7382
[Reshape] resnetv24_dense0_fwd_int8 0.99450 | 6.8046 0.99994 | 0.6719
[exDataConvert] resnetv24_dense0_fwd 0.99450 | 6.8046 0.99994 | 0.6719
```
- Note: Different platforms, different versions of tools and drivers may have slightly different results. |