Recent content by cruiser

Tried, but unfortunately it didn't help. The code I used: import tensorflow as tf def create_conv_layer(input_data, num_input_channels, num_filters, filter_shape, name): conv_filt_shape = [filter_shape[0], filter_shape[1], num_input_channels, num_filters] weights =...

Thank you for suggestion, but for some reason it still doesn't work this way. Here's full script I've tried with tf 2.5.0: import tensorflow as tf import numpy as np tf.compat.v1.disable_eager_execution() # Model consisting of 1 convolution layer input_shape = (1, 224, 224, 3) x_in =...

Thank you, it works for me as well! Maybe you know some docs regarding quantization format that is expected by Exynos 2100, so that I could quantize different networks? I am OK if I will need to write quantization on my own, but it's hard to do it without knowing what is expected format.

I've tried to run all models from Tensorflow site and this one from TFHub, but they still refuse to work properly. I've tried running them in Pro mode -> Custom model and they show same timings of ~11 ms. Could you please give a link to a standard Mobilenet-V2 with a proper quantization

I've updated my Android build and now AI Benchmark shows 2.2 ms for mobilenet v2. But I'm still unable to reproduce that in my own scripts. I also tried doing everything as in script you mentioned, but it doesn't work as well. Could you please share tflite model for mobilenet_v2 that you use in...

I am getting this number both in my own scripts (which run NNAPI using TFLite) and in AI Benchmark app. Currently in PRO Mode -> Custom Model for mobilenet-v2 I'm getting: CPU-int8: 4 ms, CPU-FP: 6.6 ms, NN-int8: 11 ms, NN-fp32: 19 ms. When I turn on Eden delegate instead of NNAPI I'm getting...

Hello @Andrey Ignatov Could you please tell a little bit more about running models with NNAPI on Exynos 2100. I tried running int8 mobilenet_v2 model using TFLite NNAPI with eden-drv accelerator, but it seems to not use accelerator (it shows timing ~12 ms, while in your benchmark its 2.2 ms)...