...
- Yocto Manual: L-813e.A13 Yocto Reference Manual (kirkstone)
- i.MX 8MP BSP Manual: LBSP Manual - 1017e.Ax phyCORE-i.MX 8M Plus BSP Manual Head(L-1017e.Ax) Head#NPU
The Celebrity Face Match Demo
| Anchor | ||||
|---|---|---|---|---|
|
...
| Scroll Title | ||||
|---|---|---|---|---|
| ||||
|
As shown in the block diagram above, we are using a pre-trained network. We are using a pre-trained network as the task of facial recognition has been very well accomplished by different research groups. We used the network from Refik Can Malli (rcmalli), which was originally trained by Q. Cao on the FaceVGG2 dataset. As rcmalli's model was written with TensorFlow 1.14.0 and Keras 2.2.4, we updated it to TensorFlow version 2.2.0. You can find the updated model here. However, we are still using the weights from the original model.
...
| Scroll Title | ||||
|---|---|---|---|---|
| ||||
|
| Scroll Title | ||||
|---|---|---|---|---|
| ||||
|
As you can see, the information gets more detailed the deeper you go into the network. The following layer inputs are composed of combinations of the previous layer outputs. in the last layers (170 of 176), you can see the information reached almost pixel-level details.
...
| Scroll Title | ||||
|---|---|---|---|---|
| ||||
|
As the neural processing unit (NPU) from NXP needs a fully int8 quantized model, we have to look into the full int8 quantization of a TensorFlow lite or PyTorch model. Both libraries are supported by the eIQ library from NXP. This manual only works with the TensorFlow variant. The general overview of how to do the post-training quantization can be found on the TensorFlow website.
...
| Scroll Title | ||||
|---|---|---|---|---|
| ||||
|
However, if we do not set the inference_input_type and inference_output_type, our model changes to:
...