问题
I have a custom built tensorflow graph implementing MobileNetV2-SSDLite which I implemented myself. It is working fine on the PC.
However, when I convert the model to TFLite (all float, no quantization), the model weights are changed drastically.
To give an example, a filter which was initially - 0.13172674179077148, 2.3185202252437188e-32, -0.003990101162344217
becomes- 4.165565013885498, -2.3981268405914307, -1.1919032335281372
The large weight values are completely throwing off my on-device inferences. Need help! :(
回答1:
What command are you using to convert to tflite? For instance are you using toco, and if so what parameters are you using? While I haven't been looking at the filters, here are my default instructions for finetuning a MobileNetV2-SSD and SSDLite graphs and the model has been performing well.
来源：https://stackoverflow.com/questions/52726632/tflite-conversion-changing-model-weights