I have created a new tflite model based on **MobilenetV2**. It works well without quantization using CPU on iOS. I should say that TensorFlow team did a great job, many thanks.

Unfortunately there is a problem with latency. I use iPhone5s to test my model, so I have the following results for CPU:

1. 500ms for MobilenetV2 with 224\*224 input image.
2. 250-300ms for MobilenetV2 with 160\*160 input image.

I used the following pod 'TensorFlowLite', '~> 1.13.1'

It's not enough, so I have read TF documentation related to optimization (post trainig quantization). I suppose I need to use Float16 or UInt8 quantization and GPU Delegate (see <https://www.tensorflow.org/lite/performance/post_training_quantization>).
I used **Tensorflow v2.1.0** to train and quantize my models.

1. **Float16 quantization of weights** (I used MobilenetV2 model after Float16 quantization)

<https://github.com/tensorflow/examples/tree/master/lite/examples/image_segmentation/ios>

* pod 'TensorFlowLiteSwift', '0.0.1-nightly'

No errors, but model doesn’t work 

* pod 'TensorFlowLiteSwift', '2.1.0'

2020-05-01 21:36:13.578369+0300 TFL Segmentation[6367:330410] Initialized TensorFlow Lite runtime.
2020-05-01 21:36:20.877393+0300 TFL Segmentation[6367:330397] Execution of the command buffer was aborted due to an error during execution. Caused GPU Hang Error (IOAF code 3)

2. **Full integer quantization** of weights and activations

pod ‘TensorFlowLiteGpuExperimental’

Code sample: <https://github.com/makeml-app/MakeML-Nails/tree/master/Segmentation%20Nails>

I used a MobilenetV2 model after uint8 quantization.

```
GpuDelegateOptions options;
    options.allow_precision_loss = true;
    options.wait_type = GpuDelegateOptions::WaitType::kActive;

    //delegate = NewGpuDelegate(nullptr);
    delegate = NewGpuDelegate(&options);

    if (interpreter->ModifyGraphWithDelegate(delegate) != kTfLiteOk)

```

Segmentation Live[6411:331887] [DYMTLInitPlatform] platform initialization successful
Loaded model 1resolved reporterDidn't find op for builtin opcode 'PAD' version '2'

Is it possible to use MObilenetV2 quantized model on IOS somehow? Hopefully I did some mistake :) and it's possible.

Best regards,
Dmitriy