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(under development) auto/crowd-tune CUDA work size (execution time)
(under development) auto/crowd-tune OpenCL local work size (execution time)
(under development) auto/crowd-tune OpenCL local work size (execution time/FPS vs energy)
(under development) crowdsource OpenCL bug detection
(under development) crowdsource modeling of program behavior
(under development) crowdsource program numerical stability
(under development) crowdsource program scalability
Collaborative Program Optimization using mobile devices
advice
ae
ae.person.table
ai-artifact
algorithm
all
announcements.funding
announcements.job
apk
artifact
auto/crowd-tune GCC compiler flags (custom dimensions)
auto/crowd-tune GCC compiler flags (do not degrade execution time, do not degrade code size)
auto/crowd-tune GCC compiler flags (minimize execution time and code size)
auto/crowd-tune GCC compiler flags (minimize execution time)
auto/crowd-tune GCC compiler flags (minimize execution time, do not degrade code size)
auto/crowd-tune GCC compiler flags (minimize total binary size, do not degrade execution time)
auto/crowd-tune LLVM compiler flags (do not degrade execution time, do not degrade code size)
auto/crowd-tune LLVM compiler flags (minimize execution time)
auto/crowd-tune LLVM compiler flags (minimize execution time, do not degrade code size)
auto/crowd-tune OpenCL-based CLBlast (GFLOPs)
autotune custom pipeline dimensions
award
caffe
caffe2
cbricks
cfg
challenge.vqe
choice
class
clblast
cmdgen
compiler
crowd-benchmark DNN libraries and models
crowd-benchmark DNN libraries and models (Caffe - dev)
crowd-benchmark DNN libraries and models (Caffe2)
crowd-benchmark DNN libraries and models (TensorFlow)
crowd-benchmark DNN libraries and models (dividiti desktop app)
crowd-benchmark DNN libraries and models using mobile devices
crowd-benchmark shared workloads via ARM WA framework
crowd-test OpenCL compilers (beta) - crowdsource bug detection via CK
crowd-test OpenGL compilers (beta)
crowdnode
dashboard
dataset
dataset.features
demo
device (deprecated or not used)
dissemination.announcement
dissemination.book
dissemination.conference
dissemination.event
dissemination.hardware
dissemination.journal
dissemination.keynote
dissemination.lecture
dissemination.patent
dissemination.pitfall
dissemination.poster
dissemination.presentation
dissemination.press-release
dissemination.publication
dissemination.publication.artifact
dissemination.repo
dissemination.soft
dissemination.workshop
docker
env
experiment
experiment.raw
experiment.scenario.android
experiment.user
experiment.view
explore DNN batch size
explore GCC compiler flags
explore LLVM compiler flags
explore OpenBLAS number of threads
explore OpenMP number of threads
explore compiler flags
fuzz GCC compiler flags (search for bugs)
fuzz LLVM compiler flags (search for bugs)
gemmbench.crowdtuning
graph
graph.dot
hackathon.20180615
hackathon.20181006
hackathon.20190127
hackathon.20190315
index
jnotebook
kernel
log
machine
math.conditions
math.frontier
math.variation
me
milepost
misc
mlperf
mlperf.inference
mlperf.mobilenets
model
model.image.classification
model.r
model.sklearn
model.species
model.tensorflowapi
model.tf
module
nntest
open ReQuEST @ ASPLOS'18 tournament (Pareto-efficient image classification)
organization
os
package
person
photo
pipeline
pipeline.cmd
platform
platform.cpu
platform.dsp
platform.gpgpu
platform.gpu
platform.init
platform.nn
platform.npu
platform.os
proceedings.acm
program
program.behavior
program.dynamic.features
program.experiment.speedup
program.optimization
program.output
program.species
program.static.features
qml
qr-code
repo
report
research.topic
result
scc-workflow
script
slide
soft
solution
sut
table
tensorflow
test
tmp
user
video
vqe
wa
wa-device
wa-params
wa-result
wa-scenario
wa-tool
web
wfe
xml
Repository:
CK (machine learning based) multi-objective autotuning
CK analytics
CK crowdtuning (crowdsourcing autotuning)
CK dissemination modules
CK repository to crowdsource optimization of benchmarks, kernels and realistic workloads across Raspberry Pi devices provided by volunteers (starting from compiler flag autotuning)
CK web
Large and shared artifacts (HOG experiments) to reproduce CK paper
Reproducible and interactive papers with all shared artifacts for our CK papers
Reproducing PAMELA project (medium data set (20 frames) for slambench) via CK
Reproducing PAMELA project (slambench analysis and crowd-tuning) via CK
Tool clsmith converted to CK format
cTuning datasets (min)
cTuning programs
cbricks
ck-ai
ck-artifact-evaluation
ck-assets
ck-caffe
ck-caffe2
ck-cntk
ck-crowd-scenarios
ck-crowdsource-dnn-optimization
ck-crowdtuning-platforms
ck-dev-compilers
ck-dissemination
ck-docker
ck-env
ck-experiments
ck-graph-analytics
ck-math
ck-mlperf
ck-mlperf-sysml-demo-20190402
ck-mxnet
ck-nntest
ck-nntest-20181001
ck-qiskit
ck-quantum
ck-quantum-challenge-vqe
ck-quantum-hackathon-20180615
ck-quantum-hackathon-20181006
ck-quantum-hackathon-20190127
ck-quantum-hackathon-20190315
ck-quantum-hackathons
ck-request
ck-request-asplos18-caffe-intel
ck-request-asplos18-iot-farm
ck-request-asplos18-mobilenets-armcl-opencl
ck-request-asplos18-mobilenets-tvm-arm
ck-request-asplos18-resnet-tvm-fpga
ck-request-asplos18-results
ck-request-asplos18-results-caffe-intel
ck-request-asplos18-results-iot-farm
ck-request-asplos18-results-mobilenets-armcl-opencl
ck-request-asplos18-results-mobilenets-tvm-arm
ck-request-asplos18-results-resnet-tvm-fpga
ck-rigetti
ck-rpi-optimization-results
ck-scc
ck-scc18
ck-tensorflow
ck-tensorrt
ck-tvm
ck-wa
ck-wa-extra
ck-wa-workloads
ck-website
ctuning-datasets
default
gemmbench
local
mlperf-mobilenets
reproduce-carp-project
reproduce-milepost-project
shader-compiler-bugs
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Meta-description in JSON:
{ "all_raw_results": [ { "behavior_uid": "ae45efb62bbbbcda", "cpu_freqs_after": [ { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 } ], "cpu_freqs_before": [ { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 } ], "image_height": 1836, "image_width": 2550, "mispredictions": [ { "correct_answer": "split pea soup", "mispredicted_image": "misprediction-image-d96462b9e6d143a6.jpg", "misprediction_results": "0.4863 - \"n07711569 mashed potato\"\n0.1496 - \"n07716906 spaghetti squash\"\n0.0867 - \"n07831146 carbonara\"\n0.0682 - \"n07614500 ice cream, icecream\"\n0.0478 - \"n07875152 potpie\"\n" }, { "correct_answer": "whiteboard", "mispredicted_image": "misprediction-image-659a621ab93a58df.jpg", "misprediction_results": "0.2806 - \"n07565083 menu\"\n0.2115 - \"n03291819 envelope\"\n0.0924 - \"n02971356 carton\"\n0.0816 - \"n02892201 brass, memorial tablet, plaque\"\n0.0709 - \"n02840245 binder, ring-binder\"\n" }, { "correct_answer": "chair", "mispredicted_image": "misprediction-image-b6fe4083968576d5.jpg", "misprediction_results": "0.1628 - \"n04517823 vacuum, vacuum cleaner\"\n0.0802 - \"n02992211 cello, violoncello\"\n0.0698 - \"n03495258 harp\"\n0.0567 - \"n03838899 oboe, hautboy, hautbois\"\n0.0411 - \"n04367480 swab, swob, mop\"\n" } ], "prediction": "0.4863 - \"n07711569 mashed potato\"\n0.1496 - \"n07716906 spaghetti squash\"\n0.0867 - \"n07831146 carbonara\"\n0.0682 - \"n07614500 ice cream, icecream\"\n0.0478 - \"n07875152 potpie\"\n", "time": [ 3985, 3969, 3995, 4647, 4286, 4538, 4307, 4543, 3968 ], "user": "", "xopenme": { "execution_time": [ 2.977872, 3.012946, 3.018679, 3.596814, 3.269021, 3.494389, 3.306191, 3.534099, 2.944817 ], "execution_time_kernel_0": [ 2.977872, 3.012946, 3.018679, 3.596814, 3.269021, 3.494389, 3.306191, 3.534099, 2.944817 ], "execution_time_kernel_1": [ 0.098725, 0.122579, 0.114403, 0.117208, 0.117385, 0.125357, 0.122052, 0.110857, 0.126897 ], "execution_time_kernel_2": [ 0.790659, 0.666718, 0.712729, 0.839577, 0.745105, 0.809695, 0.7171, 0.745702, 0.753733 ] } }, { "behavior_uid": "bad97e0df50389c4", "cpu_freqs_after": [ { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 } ], "cpu_freqs_before": [ { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 }, { "0": 1500, "1": 1500, "2": 1500, "3": 1500, "4": 2100, "5": 2100, "6": 2100, "7": 2100 } ], "image_height": 5312, "image_width": 2988, "mispredictions": [ { "correct_answer": "0.0410 - \"n04599235 wool, woolen, woollen\"", "mispredicted_image": "misprediction-image-314674b055732614.jpg", "misprediction_results": "0.3056 - \"n02504458 African elephant, Loxodonta africana\"\n0.2416 - \"n01871265 tusker\"\n0.0845 - \"n03482405 hamper\"\n0.0468 - \"n01768244 trilobite\"\n0.0410 - \"n04599235 wool, woolen, woollen\"\n" }, { "correct_answer": "0.0753 - \"n03594734 jean, blue jean, denim\"", "mispredicted_image": "misprediction-image-619426e93933314e.jpg", "misprediction_results": "0.2151 - \"n04370456 sweatshirt\"\n0.2076 - \"n04350905 suit, suit of clothes\"\n0.1094 - \"n04591157 Windsor tie\"\n0.1012 - \"n04525038 velvet\"\n0.0753 - \"n03594734 jean, blue jean, denim\"\n" }, { "correct_answer": "picture", "mispredicted_image": "misprediction-image-ee0d3203819b4e9a.jpg", "misprediction_results": "0.1533 - \"n04125021 safe\"\n0.1227 - \"n02666196 abacus\"\n0.1112 - \"n03742115 medicine chest, medicine cabinet\"\n0.0574 - \"n03529860 home theater, home theatre\"\n0.0454 - \"n04372370 switch, electric switch, electrical switch\"\n" }, { "correct_answer": "Shar Pei, dog", "mispredicted_image": "misprediction-image-728616145e4ba6b4.jpg", "misprediction_results": "0.1125 - \"n02113978 Mexican hairless\"\n0.0780 - \"n02099849 Chesapeake Bay retriever\"\n0.0550 - \"n02085620 Chihuahua\"\n0.0337 - \"n02093991 Irish terrier\"\n0.0309 - \"n02107312 miniature pinscher\"\n" }, { "correct_answer": "Shar Pei", "mispredicted_image": "misprediction-image-c94f768ecd21e13c.jpg", "misprediction_results": "0.1449 - \"n04399382 teddy, teddy bear\"\n0.0620 - \"n02113799 standard poodle\"\n0.0390 - \"n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus\"\n0.0371 - \"n02317335 starfish, sea star\"\n0.0365 - \"n02093647 Bedlington terrier\"\n" }, { "correct_answer": "remote control", "mispredicted_image": "misprediction-image-9520041264c70bfb.jpg", "misprediction_results": "0.0496 - \"n07613480 trifle\"\n0.0468 - \"n06785654 crossword puzzle, crossword\"\n0.0428 - \"n07614500 ice cream, icecream\"\n0.0354 - \"n04033901 quill, quill pen\"\n0.0238 - \"n07860988 dough\"\n" }, { "correct_answer": "0.1273 - \"n03637318 lampshade, lamp shade\"", "mispredicted_image": "misprediction-image-04658955b60c718b.jpg", "misprediction_results": "0.6880 - \"n04380533 table lamp\"\n0.1273 - \"n03637318 lampshade, lamp shade\"\n0.0564 - \"n02859443 boathouse\"\n0.0104 - \"n03788365 mosquito net\"\n0.0104 - \"n02793495 barn\"\n" }, { "correct_answer": "soda can", "mispredicted_image": "misprediction-image-ade29643c7b914a7.jpg", "misprediction_results": "0.2173 - \"n04023962 punching bag, punch bag, punching ball, punchball\"\n0.0579 - \"n03676483 lipstick, lip rouge\"\n0.0474 - \"n04557648 water bottle\"\n0.0412 - \"n04131690 saltshaker, salt shaker\"\n0.0325 - \"n03908714 pencil sharpener\"\n" }, { "correct_answer": "0.0244 - \"n04599235 wool, woolen, woollen\"", "mispredicted_image": "misprediction-image-8401a42047b8a355.jpg", "misprediction_results": "0.4035 - \"n01756291 sidewinder, horned rattlesnake, Crotalus cerastes\"\n0.1947 - \"n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus\"\n0.1137 - \"n01748264 Indian cobra, Naja naja\"\n0.0244 - \"n04599235 wool, woolen, woollen\"\n0.0208 - \"n01740131 night snake, Hypsiglena torquata\"\n" }, { "correct_answer": "door", "mispredicted_image": "misprediction-image-51a53a549e076ef8.jpg", "misprediction_results": "0.4035 - \"n04550184 wardrobe, closet, press\"\n0.1713 - \"n03742115 medicine chest, medicine cabinet\"\n0.0833 - \"n04070727 refrigerator, icebox\"\n0.0442 - \"n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin\"\n0.0297 - \"n04493381 tub, vat\"\n" }, { "correct_answer": "remote control", "mispredicted_image": "misprediction-image-0ba1f2c9074f1886.jpg", "misprediction_results": "0.1050 - \"n03259280 Dutch oven\"\n0.0436 - \"n07892512 red wine\"\n0.0399 - \"n03443371 goblet\"\n0.0359 - \"n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin\"\n0.0307 - \"n07920052 espresso\"\n" }, { "correct_answer": "blanket", "mispredicted_image": "misprediction-image-188275efafc6b514.jpg", "misprediction_results": "0.2041 - \"n02099849 Chesapeake Bay retriever\"\n0.1262 - \"n02123045 tabby, tabby cat\"\n0.0838 - \"n02091467 Norwegian elkhound, elkhound\"\n0.0632 - \"n03404251 fur coat\"\n0.0527 - \"n02110958 pug, pug-dog\"\n" }, { "correct_answer": "succulent", "mispredicted_image": "misprediction-image-83e5ecec8411a9bf.jpg", "misprediction_results": "0.6685 - \"n03991062 pot, flowerpot\"\n0.0363 - \"n07590611 hot pot, hotpot\"\n0.0281 - \"n03633091 ladle\"\n0.0259 - \"n07718472 cucumber, cuke\"\n0.0235 - \"n04596742 wok\"\n" }, { "correct_answer": "succulent", "mispredicted_image": "misprediction-image-9d9f87d3b05321ed.jpg", "misprediction_results": "0.5813 - \"n03991062 pot, flowerpot\"\n0.0853 - \"n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus\"\n0.0550 - \"n04597913 wooden spoon\"\n0.0431 - \"n03633091 ladle\"\n0.0327 - \"n03786901 mortar\"\n" }, { "correct_answer": "cord, cable", "mispredicted_image": "misprediction-image-db7d661498ca07a6.jpg", "misprediction_results": "0.3984 - \"n04332243 strainer\"\n0.0835 - \"n03627232 knot\"\n0.0452 - \"n04317175 stethoscope\"\n0.0366 - \"n03065424 coil, spiral, volute, whorl, helix\"\n0.0344 - \"n02231487 walking stick, walkingstick, stick insect\"\n" } ], "prediction": "0.3056 - \"n02504458 African elephant, Loxodonta africana\"\n0.2416 - \"n01871265 tusker\"\n0.0845 - \"n03482405 hamper\"\n0.0468 - \"n01768244 trilobite\"\n0.0410 - \"n04599235 wool, woolen, woollen\"\n", "time": [ 5167, 5219, 5075, 5150, 4932, 4953, 5277, 4823, 4666, 4760, 4892, 4455, 4093, 4585, 3926, 4926, 4839, 4754, 5786, 6421, 6282, 6224, 6490, 6778, 5792, 5807, 5593, 3971, 4097, 4261, 4741, 4295, 4699, 5038, 4727, 4607, 5514, 5463, 5381, 4938, 4948, 4518, 4671, 4442, 4418, 5525, 6022, 5549, 6662, 6664, 6502, 4432, 4307, 4435, 4904, 4964, 4472 ], "user": "", "xopenme": { "execution_time": [ 3.744293, 3.808148, 3.700086, 3.751605, 3.494191, 3.648592, 3.912035, 3.617831, 3.302027, 3.467044, 3.629758, 3.195103, 2.929848, 3.355497, 2.735307, 3.54119, 3.426615, 3.39846, 4.158904, 4.676904, 4.73925, 4.443147, 4.811692, 4.961378, 4.25562, 4.144841, 3.965349, 2.849608, 3.008902, 2.807366, 2.983332, 3.108225, 3.040869, 3.520527, 3.43272, 3.385022, 3.964869, 3.962512, 3.965733, 3.578555, 3.529118, 3.136418, 3.427098, 3.166147, 3.150589, 3.906204, 4.241818, 4.100625, 4.975897, 4.894401, 4.80427, 3.298613, 3.069002, 3.174573, 3.574593, 3.595827, 3.229566 ], "execution_time_kernel_0": [ 3.744293, 3.808148, 3.700086, 3.751605, 3.494191, 3.648592, 3.912035, 3.617831, 3.302027, 3.467044, 3.629758, 3.195103, 2.929848, 3.355497, 2.735307, 3.54119, 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"misprediction-image-594643a187083b3f.jpg", "misprediction_results": "0.1005 - \"n03379051 football helmet\"\n0.0716 - \"n09835506 ballplayer, baseball player\"\n0.0644 - \"n03763968 military uniform\"\n0.0559 - \"n04192698 shield, buckler\"\n0.0486 - \"n02817516 bearskin, busby, shako\"\n" } ], "prediction": "0.1005 - \"n03379051 football helmet\"\n0.0716 - \"n09835506 ballplayer, baseball player\"\n0.0644 - \"n03763968 military uniform\"\n0.0559 - \"n04192698 shield, buckler\"\n0.0486 - \"n02817516 bearskin, busby, shako\"\n", "time": [ 5010, 4479, 4443 ], "user": "", "xopenme": { "execution_time": [ 3.579185, 3.214869, 3.087295 ], "execution_time_kernel_0": [ 3.579185, 3.214869, 3.087295 ], "execution_time_kernel_1": [ 0.309091, 0.339617, 0.327182 ], "execution_time_kernel_2": [ 1.013226, 0.828365, 0.950093 ] } } ], "meta": { "cpu_abi": "arm64-v8a", "cpu_name": "SAMSUNG Exynos7420", "cpu_uid": "d35780d449a19831", "crowd_uid": "a7340ffbefcb5923", "engine": "Caffe CPU", "gpgpu_name": "", "gpgpu_uid": "", "gpu_name": "ARM Mali-T760", "gpu_uid": "8ea3d7446e912b92", "model": "BVLC AlexNet", "os_name": "Android 7.0", "os_uid": "de98569847a92092", "plat_name": "SAMSUNG SM-G920A", "platform_uid": "ffc3845db95edcfa" } }
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Developed by
Grigori Fursin
Implemented as a
CK workflow
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