This portal has been archived. Explore the next generation of this technology.
CK MLPerf inference 0.7 (image classification • tflite mobilenet v2 0.35 128 quantized)
solution:ck-mlperf-inference-0.7-image-classification-tflite-mobilenet-v2-0.35-128-quantized (v1.0.0)CK solution description
- Automation framework: CK
- GitHub repository: A collection of all AI and ML workflows and packages in the CK format
- Project overview: Philosophical Transactions of the Royal Society
- Documentation: online
- Results: CK dashboards , MLPerf v0.7 table
- Reproducibility: ACM/cTuning
- Source: GitHub
Installation
Follow this guide to install CK. Then pull CK repositories with AI/ML workflows and components:
ck pull repo:ai
MLPerf Inference - Image Classification - TFLite
This C++ implementation runs TFLite models for Image Classification using TFLite.
Prerequisites
Preprocess ImageNet on an x86 machine
model-tflite-mlperf-resnet*, model-tflite-mlperf-efficientnet-lite0, model-tf-and-tflite-mlperf-mobilenet* (resolution 224)
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv,side.224,full --ask
model-tf-and-tflite-mlperf-mobilenet* (resolution 192)
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv,side.192,full --ask
model-tf-and-tflite-mlperf-mobilenet* (resolution 160)
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv,side.160,full --ask
model-tf-and-tflite-mlperf-mobilenet* (resolution 128)
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv,side.128,full --ask
model-tf-and-tflite-mlperf-mobilenet* (resolution 96)
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv,side.96,full --ask
model-tflite-mlperf-efficientnet-lite1
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv,side.240,full --ask
model-tflite-mlperf-efficientnet-lite2
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv,side.260,full --ask
model-tflite-mlperf-efficientnet-lite3
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv,side.280,full --ask
model-tflite-mlperf-efficientnet-lite4
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv,side.300,full --ask
Detect ImageNet on a dev board
Copy a preprocessed ImageNet dataset onto a dev board e.g. under /datasets and register it with CK according to its resolution e.g.:
$ echo opencv-side.240 | ck detect soft --tags=dataset,imagenet,preprocessed,rgb8 \
--extra_tags=using-opencv,crop.875,full,inter.linear,side.240 \
--full_path=/datasets/dataset-imagenet-preprocessed-using-opencv-crop.875-full-inter.linear-side.240/ILSVRC2012_val_00000001.rgb8
Run once (classical CK interface)
Running this program is similar to running ck-tensorflow:program:image-classification-tflite,
as described in the MLPerf Inference repo.
firefly $ ck benchmark program:image-classification-tflite-loadgen \
--speed --repetitions=1 \
--env.CK_VERBOSE=1 \
--env.CK_LOADGEN_SCENARIO=SingleStream \
--env.CK_LOADGEN_MODE=PerformanceOnly \
--env.CK_LOADGEN_DATASET_SIZE=1024 \
--env.CK_LOADGEN_BUFFER_SIZE=1024 \
--dep_add_tags.weights=model,tflite,resnet \
--dep_add_tags.library=tflite,v1.15 \
--dep_add_tags.compiler=gcc,v7 \
--dep_add_tags.images=side.224,preprocessed \
--dep_add_tags.loadgen-config-file=image-classification-tflite \
--dep_add_tags.python=v3 \
--skip_print_timers
...
------------------------------------------------------------
| LATENCIES (in nanoseconds and fps) |
------------------------------------------------------------
Number of queries run: 1024
Min latency: 397952762ns (2.51286 fps)
Median latency: 426440993ns (2.34499 fps)
Average latency: 433287227ns (2.30794 fps)
90 percentile latency: 460194271ns (2.173 fps)
Max latency: 679467557ns (1.47174 fps)
------------------------------------------------------------
Explore different models
TODO