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Arm mlperf.org benchmark port

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# [Arm NN](https://developer.arm.com/ip-products/processors/machine-learning/arm-nn) ports for [MLPerf Inference](https://github.com/mlperf/inference) benchmarks

[![compatibility](https://github.com/ctuning/ck-guide-images/blob/master/ck-compatible.svg)](https://github.com/ctuning/ck)

- [Getting started](#gs)
    - [Install CK](#gs_ck)
    - [Pull CK repositories](#gs_repos)
    - [Install TFLite](#gs_tflite)
    - [Install ArmNN](#gs_armnn)
        - [with TFLite, Neon and OpenCL support](#gs_armnn_tflite_neon_opencl) (**recommended**)
        - [with TFLite and Neon support](#gs_armnn_tflite_neon)
        - [with TFLite and OpenCL support](#gs_armnn_tflite_opencl)
        - [with TFLite and Reference support](#gs_armnn_tflite_reference)
- [Image classification](#image_classification)
    - [Download and preprocess the ImageNet 2012 validation dataset](#imagenet)
    - [MobileNet](#mobilenet)
        - [Model](#mobilenet_model)
        - [TFLite data](#mobilenet_tflite) (reference)
        - [ArmNN Neon data](#mobilenet_armnn_neon)
        - [ArmNN OpenCL data](#mobilenet_armnn_opencl)
        - [ArmNN Reference data](#mobilenet_armnn_reference)
        - [Validate data](#mobilenet_validate)
    - [ResNet](#resnet)
        - [Model](#resnet_model)
        - [TFLite data](#resnet_tflite) (reference)
        - [ArmNN Neon data](#resnet_armnn_neon)
        - [ArmNN OpenCL data](#resnet_armnn_opencl)
        - [ArmNN Reference data](#resnet_armnn_reference)
        - [Validate data](#resnet_validate)
- [Object detection](#object_detection)
    - [Caveats](#object_detection_caveats)
    - [Download and preprocess the COCO 2017 validation dataset](#coco)
    - [SSD-MobileNet](#ssd_mobilenet)
        - [Model](#ssd_mobilenet_model)
        - [TFLite data](#ssd_mobilenet_tflite) (reference)
        - [ArmNN Neon data](#ssd_mobilenet_armnn_neon)
        - [ArmNN OpenCL data](#ssd_mobilenet_armnn_opencl)
        - [Validate data](#ssd_mobilenet_validate)

<a name="gs"></a>
# Getting started

<a name="gs_ck"></a>
## Install CK
Please follow the [CK installation instructions](https://github.com/ctuning/ck#installation).

<a name="gs_repos"></a>
## Pull CK repositories
```
$ ck pull repo --url=https://github.com/arm-software/armnn-mlperf
$ ck list repo:*armnn*
ck-armnn
armnn-mlperf
$ ck list repo:*mlperf*
ck-mlperf
armnn-mlperf
```
**NB:** Remember to refresh all the repositories after any updates (e.g. bug fixes):
```
$ ck pull all
```

<a name="gs_tflite"></a>
## Install TFLite
```
$ ck install package --tags=lib,tflite,v1.13
```

<a name="gs_armnn"></a>
## Install ArmNN
To install ArmNN with full support (frontends: TF, TFLite, ONNX; backends: Reference, OpenCL, Neon):
```
$ ck install package --tags=lib,armnn,tf,tflite,onnx,neon,opencl,rel.20.08
```
**NB:** On a platform with only a couple of GB of RAM, you way wish to restrict
the number of CPU build threads e.g. as follows:
```
$ ck install package --tags=lib,armnn,tflite,neon,opencl,rel.20.08 \
--env.CK_HOST_CPU_NUMBER_OF_PROCESSORS=4
```

If you would like to save time, you can build with TFLite frontend support only using one of the backend options below.
For more details, please refer to the [CK-ArmNN](http://github.com/ctuning/ck-armnn) repository.

<a name="gs_armnn_tflite_neon_opencl"></a>
### Option 1: Install ArmNN with TFLite, Neon and OpenCL support (**recommended**)
```
$ ck install package --tags=lib,armnn,tflite,neon,opencl,rel.20.08
```
<a name="gs_armnn_tflite_neon"></a>
### Option 2: Install ArmNN with TFLite and Neon support
```
$ ck install package --tags=lib,armnn,tflite,neon,rel.20.08
```
<a name="gs_armnn_tflite_opencl"></a>
### Option 3: Install ArmNN with TFLite and OpenCL support
```
$ ck install package --tags=lib,armnn,tflite,opencl,rel.20.08
```
<a name="gs_armnn_tflite_reference"></a>
### Option 4: ArmNN with TFLite and Reference support
```
$ ck install package --tags=lib,armnn,tflite,rel.20.08
```

<a name="image_classification"></a>
# Image classification

Please follow the MLPerf image classification instructions to install dependencies such as Python packages:
- first, the [common instructions](https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection/optional_harness_ck/classification/README.md);
- then,  the [TFLite instructions](https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection/optional_harness_ck/classification/tflite/README.md).

<a name="imagenet"></a>
## Download and preprocess the ImageNet 2012 validation dataset

### Full (50,000 images)
```
$ ck install package --tags=dataset,imagenet,val,original,full
$ ck install package --tags=dataset,imagenet,val,preprocessed,full
```

### Minimal (500 images)
```
$ ck install package --tags=dataset,imagenet,val,original,min --no_tags=resized
$ ck install package --tags=dataset,imagenet,val,preprocessed
```

### Preprocess on an x86 machine and detect on an arm dev board

The only preprocessing method that works on arm dev boards uses Pillow, which
results in significant accuracy degradation compared to the official
preprocessing method that uses OpenCV.  However, you can preprocess on an x86
machine either using OpenCV or TensorFlow, copy the resulting inputs to an arm
dev board and detect them there.

Suppose you have preprocessed the ImageNet dataset on an x86 machine in two different ways:
```
$ ck install package --tags=dataset,imagenet,val,preprocessed,using-opencv
$ ck install package --tags=dataset,imagenet,val,preprocessed,using-tensorflow
```

The preprocessed datasets can be archived into several volumes to facilitate transfer to an arm development board e.g.:
```
$ for i in {0..5}; do tar cvf dataset-imagenet-preprocessed-using-opencv.${i}.tar <dataset-imagenet-preprocessed-using-opencv-dir>/ILSVRC2012_val_000${i}*; done
$ md5sum dataset-imagenet-preprocessed-using-opencv.?.tar
```
copied over, unarchived and detected as follows:
```
$ for i in {0..5}; do tar xvf dataset-imagenet-preprocessed-using-opencv.${i}.tar; done
$ ck detect soft --tags=dataset,imagenet,preprocessed,rgb8 --extra_tags=using-opencv \
--full_path=<dataset-imagenet-preprocessed-using-opencv-dir>/ILSVRC2012_val_00000001.rgb8

$ for i in {0..5}; do tar xvf dataset-imagenet-preprocessed-using-tensorflow.${i}.tar; done
$ ck detect soft --tags=dataset,imagenet,preprocessed,rgbf32 --extra_tags=using-tensorflow \
--full_path=<dataset-imagenet-preprocessed-using-tensorflow-dir>/ILSVRC2012_val_00000001.rgbf32
```

<a name="mobilenet"></a>
## MobileNet

<a name="mobilenet_model"></a>
### Model
Install the MobileNet model:
```
$ ck install package --tags=model,tflite,mlperf,mobilenet,non-quantized
```

<a name="mobilenet_tflite"></a>
### TFLite data (reference)

#### Run on 500 images
```
$ ck benchmark program:image-classification-tflite \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=500 \
--record --record_repo=local --record_uoa=mlperf-mobilenet-tflite-accuracy-500 \
--tags=image-classification,mlperf,mobilenet,tflite,accuracy,500 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```
**NB:** You can also run the same command on the full ImageNet validation dataset of 50,000 images (see below).

#### Run on 50,000 images
```
$ ck benchmark program:image-classification-tflite \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
--record --record_repo=local --record_uoa=mlperf-mobilenet-tflite-accuracy-50000 \
--tags=image-classification,mlperf,mobilenet,tflite,accuracy,50000 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="mobilenet_armnn_neon"></a>
### ArmNN Neon data

#### Run on 500 images
```
$ ck benchmark program:image-classification-armnn-tflite --env.USE_NEON \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=500 \
--record --record_repo=local --record_uoa=mlperf-mobilenet-armnn-tflite-accuracy-neon-500 \
--tags=image-classification,mlperf,mobilenet,armnn-tflite,accuracy,neon,500 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

#### Run on 50,000 images
```
$ ck benchmark program:image-classification-armnn-tflite --env.USE_NEON \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
--record --record_repo=local --record_uoa=mlperf-mobilenet-armnn-tflite-accuracy-neon-50000 \
--tags=image-classification,mlperf,mobilenet,armnn-tflite,accuracy,neon,50000 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="mobilenet_armnn_opencl"></a>
### ArmNN OpenCL data

#### Run on 500 images
```
$ ck benchmark program:image-classification-armnn-tflite --env.USE_OPENCL \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=500 \
--record --record_repo=local --record_uoa=mlperf-mobilenet-armnn-tflite-accuracy-opencl-500 \
--tags=image-classification,mlperf,mobilenet,armnn-tflite,accuracy,opencl,500 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

#### Run on 50,000 images
```
$ ck benchmark program:image-classification-armnn-tflite --env.USE_OPENCL \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
--record --record_repo=local --record_uoa=mlperf-mobilenet-armnn-tflite-accuracy-opencl-50000 \
--tags=image-classification,mlperf,mobilenet,armnn-tflite,accuracy,opencl,50000 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="mobilenet_armnn_reference"></a>
### ArmNN Reference data (**NOT RECOMMENDED**)

**NB:** This validation can run on x86 or arm. However, it is completely unoptimized and hence extremely slow (e.g. 6.5 seconds per image on a Linaro HiKey960 board or 2.9 seconds per image on an Intel Xeon server).

#### Run on 500 images
```
$ ck benchmark program:image-classification-armnn-tflite \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=500 \
--record --record_repo=local --record_uoa=mlperf-mobilenet-armnn-tflite-accuracy-500 \
--tags=image-classification,mlperf,mobilenet,armnn-tflite,accuracy,500 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

#### Run on 50,000 images
```
$ ck benchmark program:image-classification-armnn-tflite \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
--record --record_repo=local --record_uoa=mlperf-mobilenet-armnn-tflite-accuracy-50000 \
--tags=image-classification,mlperf,mobilenet,armnn-tflite,accuracy,50000 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="mobilenet_validate"></a>
### Validate experimental data

To validate the equivalence of the optimized ArmNN implementation versus the reference TFLite one,
we collected experimental data as above on two platforms:
- A Linaro [HiKey960](https://www.96boards.org/product/hikey960/) board (`hikey`): TFLite vs. ArmNN Neon vs. ArmNN OpenCL (500 and 50,000 images).
- An Intel Xeon server (`velociti`): TFLite vs. ArmNN Reference (500 and 50,000 images).

The resulting experimental entries were archived e.g. as follows:
```bash
hikey$ ck list local:experiment:mlperf-mobilenet*accuracy*500
mlperf-mobilenet-tflite-accuracy-500
mlperf-mobilenet-armnn-tflite-accuracy-neon-500
mlperf-mobilenet-armnn-tflite-accuracy-opencl-500
hikey$ ck zip local:experiment:mlperf-mobilenet*accuracy*500 \
                --archive_name=mlperf-mobilenet-accuracy-500-hikey.zip
```
The archives were then uploaded to DropBox.
You can follow instructions below to download the archives and validate the accuracy.

#### `hikey`
##### 500 images
```bash
$ wget https://www.dropbox.com/s/9lz7yncy1xtqlvj/mlperf-mobilenet-accuracy-500-hikey.zip
$ ck add repo --zip=mlperf-mobilenet-accuracy-500-hikey.zip
$ ck list --repo_uoa=mlperf-mobilenet-accuracy-500-hikey --print_full
mlperf-mobilenet-accuracy-500-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-neon-500
mlperf-mobilenet-accuracy-500-hikey:experiment:mlperf-mobilenet-tflite-accuracy-500
mlperf-mobilenet-accuracy-500-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-opencl-500
```
###### TFLite vs. ArmNN Neon
```bash
$ ck compare_experiments mlperf \
mlperf-mobilenet-accuracy-500-hikey:experiment:mlperf-mobilenet-tflite-accuracy-500 \
mlperf-mobilenet-accuracy-500-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-neon-500
...
{'epsilon': 1e-05,
 'max_delta': 7.000000000034756e-06,
 'num_mismatched_classes': 0,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 0,
 'num_mismatched_probabilities': 0,
 'return': 0}
```
###### TFLite vs. ArmNN OpenCL
```bash
$ ck compare_experiments mlperf \
mlperf-mobilenet-accuracy-500-hikey:experiment:mlperf-mobilenet-tflite-accuracy-500 \
mlperf-mobilenet-accuracy-500-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-opencl-500
...
{'epsilon': 1e-05,
 'max_delta': 9.000000000036756e-06,
 'num_mismatched_classes': 0,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 0,
 'num_mismatched_probabilities': 0,
 'return': 0}
```
###### ArmNN Neon vs. ArmNN OpenCL
```bash
$ ck compare_experiments mlperf \
mlperf-mobilenet-accuracy-500-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-neon-500 \
mlperf-mobilenet-accuracy-500-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-opencl-500
...
{'epsilon': 1e-05,
 'max_delta': 6.0000000000060005e-06,
 'num_mismatched_classes': 0,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 0,
 'num_mismatched_probabilities': 0,
 'return': 0}
```

##### 50,000 images
```bash
$ wget https://www.dropbox.com/s/3cdi3lx7jfxwse7/mlperf-mobilenet-accuracy-50000-hikey.zip
$ ck add repo --zip=mlperf-mobilenet-accuracy-50000-hikey.zip
$ ck list --repo_uoa=mlperf-mobilenet-accuracy-50000-hikey --print_full
mlperf-mobilenet-accuracy-50000-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-neon-50000
mlperf-mobilenet-accuracy-50000-hikey:experiment:mlperf-mobilenet-tflite-accuracy-50000
mlperf-mobilenet-accuracy-50000-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-opencl-50000
```
###### TFLite vs. ArmNN Neon
```bash
$ ck compare_experiments mlperf \
mlperf-mobilenet-accuracy-50000-hikey:experiment:mlperf-mobilenet-tflite-accuracy-50000 \
mlperf-mobilenet-accuracy-50000-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-neon-50000
...
{'epsilon': 1e-05,
 'max_delta': 1.3000000000040757e-05,
 'num_mismatched_classes': 10,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 20,
 'num_mismatched_probabilities': 19,
 'return': 0}
```
###### TFLite vs. ArmNN OpenCL
```bash
$ ck compare_experiments mlperf \
mlperf-mobilenet-accuracy-50000-hikey:experiment:mlperf-mobilenet-tflite-accuracy-50000 \
mlperf-mobilenet-accuracy-50000-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-opencl-50000
...
{'epsilon': 1e-05,
 'max_delta': 1.4000000000014001e-05,
 'num_mismatched_classes': 8,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 20,
 'num_mismatched_probabilities': 18,
 'return': 0}
```
###### ArmNN Neon vs. ArmNN OpenCL
```bash
$ ck compare_experiments mlperf \
mlperf-mobilenet-accuracy-50000-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-neon-50000 \
mlperf-mobilenet-accuracy-50000-hikey:experiment:mlperf-mobilenet-armnn-tflite-accuracy-opencl-50000
...
Checking ILSVRC2012_val_00033823.JPEG...
- mismatched classes at index 2: 137 != 136
- mismatched classes at index 3: 136 != 137
...
{'epsilon': 1e-05,
 'max_delta': 8.000000000008e-06,
 'num_mismatched_classes': 2,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 1,
 'num_mismatched_probabilities': 0,
 'return': 0}
```

#### `velociti`
##### 500 images
```bash
$ wget https://www.dropbox.com/s/j2rdh3uzhz3lqh7/mlperf-mobilenet-accuracy-500-velociti.zip
$ ck add repo --zip=mlperf-mobilenet-accuracy-500-velociti.zip
$ ck list --repo_uoa=mlperf-mobilenet-accuracy-500-velociti --print_full
mlperf-mobilenet-accuracy-500-velociti:experiment:mlperf-mobilenet-armnn-tflite-accuracy-500
mlperf-mobilenet-accuracy-500-velociti:experiment:mlperf-mobilenet-tflite-accuracy-500
```
###### TFLite vs. ArmNN Reference
```
$ ck compare_experiments mlperf \
mlperf-mobilenet-accuracy-500-velociti:experiment:mlperf-mobilenet-armnn-tflite-accuracy-500 \
mlperf-mobilenet-accuracy-500-velociti:experiment:mlperf-mobilenet-tflite-accuracy-500
...
{'epsilon': 1e-05,
 'max_delta': 7.000000000090267e-06,
 'num_mismatched_classes': 0,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 0,
 'num_mismatched_probabilities': 0,
 'return': 0}
```

##### 50,000 images
```bash
$ wget https://www.dropbox.com/s/z5bx7aeocwdyrww/mlperf-mobilenet-accuracy-50000-velociti.zip
$ ck add repo --zip=mlperf-mobilenet-accuracy-50000-velociti.zip
$ ck list --repo_uoa=mlperf-mobilenet-accuracy-50000-velociti --print_full
mlperf-mobilenet-accuracy-50000-velociti:experiment:mlperf-mobilenet-armnn-tflite-accuracy-50000
mlperf-mobilenet-accuracy-50000-velociti:experiment:mlperf-mobilenet-tflite-accuracy-50000
```
###### TFLite vs. ArmNN Reference
```
$ ck compare_experiments mlperf \
mlperf-mobilenet-accuracy-50000-velociti:experiment:mlperf-mobilenet-armnn-tflite-accuracy-50000 \
mlperf-mobilenet-accuracy-50000-velociti:experiment:mlperf-mobilenet-tflite-accuracy-50000
...
{'epsilon': 1e-05,
 'max_delta': 1.2000000000012001e-05,
 'num_mismatched_classes': 2,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 14,
 'num_mismatched_probabilities': 17,
 'return': 0}
```

<a name="resnet"></a>
## ResNet

<a name="resnet_model"></a>
### Model
Install the ResNet model:
```
$ ck install package --tags=model,tflite,mlperf,resnet

More than one package or version found:

0) model-tflite-mlperf-resnet-no-argmax  Version 1.5  (afb43014ef38f646)
 1) model-tflite-mlperf-resnet  Version 1.5  (d60d4e9a84151271)
 2) model-tflite-convert-from-tf (35e84375ac48dcb1), Variations: resnet

Please select the package to install [ hit return for "0" ]:
```

Option 0 will download a TFLite model preconverted from the TF model.  During
the conversion, the `ArgMax` operator causing an
[issue](https://github.com/ARM-software/armnn/issues/150) with ArmNN v19.02 was
excluded.

Option 1 will download a TFLite model preconverted from the TF model, but
including the `ArgMax` operator. This variant can be used once the above issue
is resolved.

Option 2 will download the TF model and convert it to TFLite, while excluding
the `ArgMax` operator.  Since the conversion relies on a prebuilt version of
TF, this option is only viable on x86.  (This constraint can be relaxed, but
building TF on Arm [is not officially
supported](https://github.com/tensorflow/tensorflow/issues/25607#issuecomment-466583730).)

<a name="resnet_tflite"></a>
### TFLite data (reference)

#### Run on 500 images
```
$ ck benchmark program:image-classification-tflite \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=500 \
--record --record_repo=local --record_uoa=mlperf-resnet-tflite-accuracy-500 \
--tags=image-classification,mlperf,resnet,tflite,accuracy,500 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```
**NB:** You can also run the same command on the full ImageNet validation dataset of 50,000 images (see below).

#### Run on 50,000 images
```
$ ck benchmark program:image-classification-tflite \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
--record --record_repo=local --record_uoa=mlperf-resnet-tflite-accuracy-50000 \
--tags=image-classification,mlperf,resnet,tflite,accuracy,50000 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="resnet_armnn_neon"></a>
### ArmNN Neon data

#### Run on 500 images
```
$ ck benchmark program:image-classification-armnn-tflite --env.USE_NEON \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=500 \
--record --record_repo=local --record_uoa=mlperf-resnet-armnn-tflite-accuracy-neon-500 \
--tags=image-classification,mlperf,resnet,armnn-tflite,accuracy,neon,500 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

#### Run on 50,000 images
```
$ ck benchmark program:image-classification-armnn-tflite --env.USE_NEON \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
--record --record_repo=local --record_uoa=mlperf-resnet-armnn-tflite-accuracy-neon-50000 \
--tags=image-classification,mlperf,resnet,armnn-tflite,accuracy,neon,50000 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="resnet_armnn_opencl"></a>
### ArmNN OpenCL data

#### Run on 500 images
```
$ ck benchmark program:image-classification-armnn-tflite --env.USE_OPENCL \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=500 \
--record --record_repo=local --record_uoa=mlperf-resnet-armnn-tflite-accuracy-opencl-500 \
--tags=image-classification,mlperf,resnet,armnn-tflite,accuracy,opencl,500 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

#### Run on 50,000 images
```
$ ck benchmark program:image-classification-armnn-tflite --env.USE_OPENCL \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
--record --record_repo=local --record_uoa=mlperf-resnet-armnn-tflite-accuracy-opencl-50000 \
--tags=image-classification,mlperf,resnet,armnn-tflite,accuracy,opencl,50000 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="resnet_armnn_reference"></a>
### ArmNN Reference data (**NOT RECOMMENDED**)

**NB:** This validation can run on x86 or arm. However, it is completely unoptimized and hence extremely slow (e.g. 34.8 seconds per image on a Linaro HiKey960 board or 16.9 seconds per image on an Intel Xeon server).

#### Run on 500 images
```
$ ck benchmark program:image-classification-armnn-tflite \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=500 \
--record --record_repo=local --record_uoa=mlperf-resnet-armnn-tflite-accuracy-500 \
--tags=image-classification,mlperf,resnet,armnn-tflite,accuracy,500 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

#### Run on 50,000 images (**NOT RUN**)
```
$ ck benchmark program:image-classification-armnn-tflite \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
--record --record_repo=local --record_uoa=mlperf-resnet-armnn-tflite-accuracy-50000 \
--tags=image-classification,mlperf,resnet,armnn-tflite,accuracy,50000 \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="resnet_validate"></a>
### Validate experimental data

The resulting experimental entries were archived e.g. as follows:
```bash
hikey$ ck list local:experiment:mlperf-resnet*accuracy*500
mlperf-resnet-tflite-accuracy-500
mlperf-resnet-armnn-tflite-accuracy-neon-500
mlperf-resnet-armnn-tflite-accuracy-opencl-500
hikey$ ck zip local:experiment:mlperf-resnet*accuracy*500 \
                --archive_name=mlperf-resnet-accuracy-500-hikey.zip
```
The archives were then uploaded to DropBox.
You can follow instructions below to download the archives and validate the accuracy.

#### `hikey`
##### 500 images
```bash
$ wget https://www.dropbox.com/s/eod0bflxxzpudmr/mlperf-resnet-accuracy-500-hikey.zip
$ ck add repo --zip=mlperf-resnet-accuracy-500-hikey.zip
$ ck list --repo_uoa=mlperf-resnet-accuracy-500-hikey --print_full
mlperf-resnet-accuracy-500-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-neon-500
mlperf-resnet-accuracy-500-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-opencl-500
mlperf-resnet-accuracy-500-hikey:experiment:mlperf-resnet-tflite-accuracy-500
```
###### TFLite vs. ArmNN Neon
```bash
$ ck compare_experiments mlperf \
mlperf-resnet-accuracy-500-hikey:experiment:mlperf-resnet-tflite-accuracy-500 \
mlperf-resnet-accuracy-500-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-neon-500
...
{'epsilon': 1e-05,
 'max_delta': 1.0999999999983245e-05,
 'num_mismatched_classes': 0,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 2,
 'num_mismatched_probabilities': 3,
 'return': 0}
```
###### TFLite vs. ArmNN OpenCL
```bash
$ ck compare_experiments mlperf \
mlperf-resnet-accuracy-500-hikey:experiment:mlperf-resnet-tflite-accuracy-500 \
mlperf-resnet-accuracy-500-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-opencl-500
...
{'epsilon': 1e-05,
 'max_delta': 1.0999999999983245e-05,
 'num_mismatched_classes': 0,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 3,
 'num_mismatched_probabilities': 4,
 'return': 0}
```
###### ArmNN Neon vs. ArmNN OpenCL
```bash
$ ck compare_experiments mlperf \
mlperf-resnet-accuracy-500-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-neon-500 \
mlperf-resnet-accuracy-500-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-opencl-500
...
{'epsilon': 1e-05,
 'max_delta': 6.0000000000060005e-06,
 'num_mismatched_classes': 0,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 0,
 'num_mismatched_probabilities': 0,
 'return': 0}
```

##### 50,000 images
```bash
$ wget https://www.dropbox.com/s/1yzv6unriqs18yb/mlperf-resnet-accuracy-50000-hikey.zip
$ ck add repo --zip=mlperf-resnet-accuracy-50000-hikey.zip
$ ck list --repo_uoa=mlperf-resnet-accuracy-50000-hikey --print_full
mlperf-resnet-accuracy-50000-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-opencl-50000
mlperf-resnet-accuracy-50000-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-neon-50000
mlperf-resnet-accuracy-50000-hikey:experiment:mlperf-resnet-tflite-accuracy-50000
```
###### TFLite vs. ArmNN Neon
```bash
$ ck compare_experiments mlperf \
mlperf-resnet-accuracy-50000-hikey:experiment:mlperf-resnet-tflite-accuracy-50000 \
mlperf-resnet-accuracy-50000-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-neon-50000
...
{'epsilon': 1e-05,
 'max_delta': 2.4000000000024002e-05,
 'num_mismatched_classes': 6,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 107,
 'num_mismatched_probabilities': 153,
 'return': 0}
```
###### TFLite vs. ArmNN OpenCL
```bash
$ ck compare_experiments mlperf \
mlperf-resnet-accuracy-50000-hikey:experiment:mlperf-resnet-tflite-accuracy-50000 \
mlperf-resnet-accuracy-50000-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-opencl-50000
...
{'epsilon': 1e-05,
 'max_delta': 2.5000000000052758e-05,
 'num_mismatched_classes': 4,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 147,
 'num_mismatched_probabilities': 190,
 'return': 0}
```
###### ArmNN Neon vs. ArmNN OpenCL
```bash
$ ck compare_experiments mlperf \
mlperf-resnet-accuracy-50000-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-neon-50000 \
mlperf-resnet-accuracy-50000-hikey:experiment:mlperf-resnet-armnn-tflite-accuracy-opencl-50000
...
{'epsilon': 1e-05,
 'max_delta': 1.0000000000010001e-05,
 'num_mismatched_classes': 6,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 5,
 'num_mismatched_probabilities': 2,
 'return': 0}
```

#### `velociti`
##### 500 images
```bash
$ wget https://www.dropbox.com/s/1jv4lpfp1ddr2j7/mlperf-resnet-accuracy-500-velociti.zip
$ ck add repo --zip=mlperf-resnet-accuracy-500-velociti.zip
$ ck list --repo_uoa=mlperf-resnet-accuracy-500-velociti --print_full
mlperf-resnet-accuracy-500-velociti:experiment:mlperf-resnet-armnn-tflite-accuracy-500
mlperf-resnet-accuracy-500-velociti:experiment:mlperf-resnet-tflite-accuracy-500
```
###### TFLite vs. ArmNN Reference
```
$ ck compare_experiments mlperf \
mlperf-resnet-accuracy-500-velociti:experiment:mlperf-resnet-armnn-tflite-accuracy-500 \
mlperf-resnet-accuracy-500-velociti:experiment:mlperf-resnet-tflite-accuracy-500
...
{'epsilon': 1e-05,
 'max_delta': 3.0000000000030003e-06,
 'num_mismatched_classes': 0,
 'num_mismatched_elementary_keys': 0,
 'num_mismatched_files': 0,
 'num_mismatched_probabilities': 0,
 'return': 0}
```

<a name="object_detection"></a>
# Object detection

Please follow the MLPerf object detection instructions to install dependencies such as Python packages:
- first, the [common instructions](https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection/optional_harness_ck/detection/README.md);
- then,  the [TFLite instructions](https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection/optional_harness_ck/detection/tflite/README.md).

<a name="object_detection_caveats"></a>
## Caveats

### TFLite
The SSD models require TFLite 1.13.1.

### Python 3

The [COCO API](https://github.com/cocodataset/cocoapi) (used to evaluate object detection accuracy on the [COCO dataset](http://cocodataset.org/)) requires Python 3.
Since many embedded platforms use Python 2 by default (including HiKey960), [care must be taken](https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection/optional_harness_ck/detection#install-python-3-and-the-latest-pip) not to mix Python 3 and Python 2 packages.
Therefore, all benchmarking commands below use the `CK_PYTHON=python3` prefix to ensure CK runs under Python 3.

<a name="coco"></a>
## Download and preprocess the COCO 2017 validation dataset
```bash
$ ck install package --tags=object-detection,dataset,coco.2017,val,original,full
$ ck install package --tags=object-detection,dataset,coco.2017,preprocessed,full
```

<a name="ssd_mobilenet"></a>
## SSD-MobileNet

<a name="ssd_mobilenet_model"></a>
### Model
Install the SSD-MobileNet model:
```bash
$ ck install package --tags=model,tflite,mlperf,object-detection,ssd-mobilenet
```

<a name="ssd_mobilenet_tflite"></a>
### TFLite data (reference)
```bash
$ CK_PYTHON=python3 ck benchmark program:object-detection-tflite \
--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=5000 --env.CK_METRIC_TYPE=COCO \
--record --record_repo=local --record_uoa=mlperf-object-detection-ssd-mobilenet-tflite-accuracy \
--tags=mlperf,object-detection,ssd-mobilenet,tflite,accuracy \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="ssd_mobilenet_armnn_neon"></a>
### ArmNN Neon data
```bash
$ CK_PYTHON=python3 ck benchmark program:object-detection-armnn-tflite --env.USE_NMS=regular --env.USE_NEON \
--speed --repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=5000 --env.CK_METRIC_TYPE=COCO \
--record --record_repo=local --record_uoa=mlperf-object-detection-ssd-mobilenet-armnn-tflite-accuracy-neon \
--tags=mlperf,object-detection,ssd-mobilenet,armnn-tflite,accuracy,neon \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="ssd_mobilenet_armnn_opencl"></a>
### ArmNN OpenCL data
```bash
$ CK_PYTHON=python3 ck benchmark program:object-detection-armnn-tflite --env.USE_NMS=regular --env.USE_OPENCL \
--speed --repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=5000 --env.CK_METRIC_TYPE=COCO \
--record --record_repo=local --record_uoa=mlperf-object-detection-ssd-mobilenet-armnn-tflite-accuracy-opencl \
--tags=mlperf,object-detection,ssd-mobilenet,armnn-tflite,accuracy,opencl \
--skip_print_timers --skip_stat_analysis --process_multi_keys
```

<a name="ssd_mobilenet_validate"></a>
### Validate experimental data

To validate the equivalence of the optimized ArmNN implementation versus the reference TFLite one,
we collected experimental data as above on:
- A Linaro [HiKey960](https://www.96boards.org/product/hikey960/) board (`hikey`): TFLite vs. ArmNN Neon vs. ArmNN OpenCL.

The resulting experimental entries were archived e.g. as follows:
```bash
hikey$ ck list local:experiment:mlperf-object-detection-ssd-mobilenet*accuracy*
...
hikey$ ck zip local:experiment:mlperf-object-detection-ssd-mobilenet*accuracy* \
                --archive_name=mlperf-object-detection-ssd-mobilenet-accuracy-hikey.zip
```
The archives were then uploaded to DropBox.
You can follow instructions below to download the archives and validate the accuracy.

#### hikey
```
$ wget https://www.dropbox.com/s/jzpum9fedwgq8rd/mlperf-object-detection-ssd-mobilenet-accuracy-hikey.zip
$ ck add repo --zip=mlperf-object-detection-ssd-mobilenet-accuracy-hikey.zip
$ ck list --repo_uoa=mlperf-object-detection-ssd-mobilenet-accuracy-hikey --print_full
mlperf-object-detection-ssd-mobilenet-accuracy-hikey:experiment:mlperf-object-detection-ssd-mobilenet-armnn-tflite-accuracy-opencl
mlperf-object-detection-ssd-mobilenet-accuracy-hikey:experiment:mlperf-object-detection-ssd-mobilenet-armnn-tflite-accuracy-neon
mlperf-object-detection-ssd-mobilenet-accuracy-hikey:experiment:mlperf-object-detection-ssd-mobilenet-tflite-accuracy
```

##### TFLite vs. ArmNN Neon
```
$ ck compare_experiments mlperf \
mlperf-object-detection-ssd-mobilenet-accuracy-hikey:experiment:mlperf-object-detection-ssd-mobilenet-tflite-accuracy \
mlperf-object-detection-ssd-mobilenet-accuracy-hikey:experiment:mlperf-object-detection-ssd-mobilenet-armnn-tflite-accuracy-neon
...
{'delta_mAP': 0.0007913466136450775,
 'delta_recall': 0.001550580396864898,
 'epsilon_bbox': [1.0, 1.0, 1.0, 1.0],
 'epsilon_dist': 100.0,
 'epsilon_prob': 1e-05,
 'max_delta_bbox': [596.98, 490.93, 606.36, 583.04],
 'max_delta_dist': 173810.622875,
 'max_delta_prob': 0.49000000000000005,
 'num_mismatched_classes': 239,
 'num_mismatched_distances': 1353,
 'num_mismatched_files': 1166,
 'num_mismatched_probabilities': 442,
 'return': 0}
```

##### TFLite vs. ArmNN OpenCL
```
$ ck compare_experiments mlperf \
mlperf-object-detection-ssd-mobilenet-accuracy-hikey:experiment:mlperf-object-detection-ssd-mobilenet-tflite-accuracy \
mlperf-object-detection-ssd-mobilenet-accuracy-hikey:experiment:mlperf-object-detection-ssd-mobilenet-armnn-tflite-accuracy-opencl
...
{'delta_mAP': 0.0007900779499596944,
 'delta_recall': 0.0015451924658304583,
 'epsilon_bbox': [1.0, 1.0, 1.0, 1.0],
 'epsilon_dist': 100.0,
 'epsilon_prob': 1e-05,
 'max_delta_bbox': [596.98, 490.93, 606.36, 583.04],
 'max_delta_dist': 173810.622875,
 'max_delta_prob': 0.49000000000000005,
 'num_mismatched_classes': 239,
 'num_mismatched_distances': 1353,
 'num_mismatched_files': 1167,
 'num_mismatched_probabilities': 444,
 'return': 0}
```

##### ArmNN Neon vs. ArmNN OpenCL
```
$ ck compare_experiments mlperf \
mlperf-object-detection-ssd-mobilenet-accuracy-hikey:experiment:mlperf-object-detection-ssd-mobilenet-armnn-tflite-accuracy-neon \
mlperf-object-detection-ssd-mobilenet-accuracy-hikey:experiment:mlperf-object-detection-ssd-mobilenet-armnn-tflite-accuracy-opencl
...
{'delta_mAP': -1.2686636853831423e-06,
 'delta_recall': -5.387931034439575e-06,
 'epsilon_bbox': [1.0, 1.0, 1.0, 1.0],
 'epsilon_dist': 100.0,
 'epsilon_prob': 1e-05,
 'max_delta_bbox': [0.010000000000047748,
                    0.010000000000047748,
                    0.010000000000047748,
                    0.010000000000047748],
 'max_delta_dist': 3.1188250000122935,
 'max_delta_prob': 0.0010000000000000009,
 'num_mismatched_classes': 0,
 'num_mismatched_distances': 0,
 'num_mismatched_files': 7,
 'num_mismatched_probabilities': 7,
 'return': 0}
```

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