kleinyuan / tensorflow-yolo-ios Goto Github PK

Real time object detection demo App with Yolo on iOS based on tensorflow framework

License: MIT License

CMake 2.15% Objective-C 28.55% C++ 37.96% Shell 0.27% Cuda 0.38% C 0.57% Forth 1.38% Python 0.35% JavaScript 0.11% CSS 0.05% Protocol Buffer 3.02% Ruby 0.03% PureBasic 0.03% Swift 21.42% Objective-C++ 3.73%

deep-learning yolo tiny-yolo ios swift tensorflow darknet real-time deep-neural-networks deepbelief

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johndpope n2itn fsmanji wellth-app bluehackrano biranchi2018 initialz tony32769

tensorflow-yolo-ios's Issues

Training / Converting Model

First off, thank you for putting this repo together!

In preparation for training my own model on YOLO and converting to TF I wanted to prove out the pattern by taking the TinyYOLO VOC weights/config and recreating the frozen memmapped graph that you provide in this repo. After going through the Darkflow docs as well as Tensorflow for Mobile Poets I came up with the following process for converting the graph. The graph loads and runs on my iPhone; however, it seems to just randomly identify non-objects (typically 20-30 per second) which inevitably runs out of memory and crashes the application. I'll provide the steps I took for converting the graph below. Could you please share how you created your graph or provide some feedback?

BTW, the first big red flag I see is when I freeze the TinyYOLO VOC weights using Darkflow my graph is 61MB, but yours is 180MB?!

Here's my process:

Grab TinyYOLO VOC weights and config (I've used PJ's config as well as yours, there are a few differences):

wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/tiny-yolo-voc.cfg
wget https://pjreddie.com/media/files/tiny-yolo-voc.weights

Next I validate that the weights/configs work before freezing:

flow --model tiny-yolo-voc.cfg --load tiny-yolo-voc.weights --gpu 0.9 --json

Next I freeze the graph and validate it still identifies the objects (everything works great):

flow --model tiny-yolo-voc.cfg --load tiny-yolo-voc.weights --savepb --gpu 0.9
flow --pbLoad built_graph/tiny-yolo-voc.pb --metaLoad built_graph/tiny-yolo-voc.meta --imgdir sample_img/ --json --gpu 0.9

Then I optimize the graph for inference and test again (still works):

bazel-bin/tensorflow/python/tools/optimize_for_inference \
    --input=/yoloz/tiny/built_graph/tiny-yolo-voc.pb \
    --output=/yoloz/tiny/built_graph/optimized_graph.pb \
     --input_names=input \
     --output_names=output \
     --frozen_graph=True

Then I quantize and round the graph and validate (still works, but some accuracy drops as expected):

bazel-bin/tensorflow/tools/quantization/quantize_graph \
--input=/yoloz/tiny/built_graph/optimized_graph.pb \
--output=/yoloz/tiny/built_graph/rounded_graph.pb \
--output_node_names=output \
--mode=weights_rounded

Finally I enable memory mapping in the graph:

bazel-bin/tensorflow/contrib/util/convert_graphdef_memmapped_format \
--in_graph=/yoloz/tiny/built_graph/rounded_graph.pb \
--out_graph=/yoloz/tiny/built_graph/mmapped_graph.pb

I've tried cutting out steps such as quantizing and memory mapping, but I still run into the same issue where it just randomly identifies non-existent objects. Here's an example from the iOS logs of it just seeing tons of non-existent objects:

48 boxes
cow 0.26528638082959 (-716.969760456415, -15.0664010845897, 2253.67373242581, 124.151942685188)
person 60.9147208802824 (406.176437143808, 247.405798095825, 109.771308640561, 79.8200096378618)
sheep 39.6371332656176 (701.706673330065, 173.24449355272, 162.208712026626, 0.201273832795577)
aeroplane 14.7475192095906 (422.813935954874, -672.132180748015, 0.435470956695015, 1851.95653674813)
bottle 34.4575969911148 (-3691.35341221254, 230.732243466384, 7407.03830472379, 118.209991155339)
bottle 15.252358611848 (297.912561621646, 253.622715234777, 103.666117939307, 156.487766640486)
person 2.02568703175324 (-1223.67370049141, 403.496686780806, 2378.22481550453, 17.7921825504054)
person 46.802289779278 (1029.42971074394, 59.8810085550473, 0.0603049292979604, 6.32803476229502)
person 5.02492657361699 (-242.108751411802, -179.995665610159, 768.943885578703, 172.626105837104)
bottle 8.12876115292863 (1.31502027136079, 112.241214824463, 83.795759190395, 137.321676756878)
bottle 17.4926418818899 (-2195.20377769767, 44.7168849437375, 5036.41495845117, 109.233492394215)
bottle 10.0492816390524 (-348.11391411164, -823.872093321802, 1358.45449526415, 976.078075378695)
horse 11.7777611150507 (-5199.91062956631, 209.564810465238, 10445.2793049104, 193.588701516892)
sofa 2.90329754699323 (-580.55927439087, -1201.57429169997, 605.32719492025, 2319.642035421)
sheep 2.31771116209555 (335.281302477022, -265.169598244711, 303.744002610247, 615.620747373669)
bus 10.6498479187201 (-24099.0875846469, -55.1337276379371, 48149.893631693, 531.781720515113)
person 2.18346198169684 (245.163570981687, -659.499791728503, 452.623426099545, 547.663504813206)
tvmonitor 0.347630301506097 (250.864021547363, 230.171159402745, 178.092564045225, 107.932911465985)
bottle 9.95446473408515 (578.688593486094, 225.090434384003, 29.7744054641031, 5.09407076904225)
cow 9.39130423078825 (751.656599451878, 494.282862500461, 1.09127926019101, 0.0376114695453585)
cow 16.8039449513635 (-636.942771411139, -4146.12791663897, 105.520065759871, 8539.38077769234)
cow 15.3530701945051 (10.8899058785902, -2004.09817131406, 28.3976730276271, 4464.49136457081)
cow 6.18686360683682 (-1872.86129217465, 317.97697050232, 4741.10130713052, 5.93505212236634)
cow 41.8923066953403 (-5090.64552303113, -433.732989685124, 11922.7837276564, 842.91523490483)
person 7.01425882730575 (196.017857693368, 10.762791441239, 0.29155214652136, 133.058044741693)
cow 21.7848502025759 (-215.860215658783, -249.177102701316, 267.580321846654, 392.007555619633)
tvmonitor 5.30925632140537 (528.546262539762, 472.202107788961, 27.6917343533151, 152.522967437084)
tvmonitor 5.94156905219603 (565.858886985312, -920.189522495108, 318.533761354198, 2161.87640869381)
bus 8.98683072242511 (-6645.9226178394, 139.156692264233, 14659.6244466323, 374.677935877221)
cow 8.49134918033451 (432.454714344806, -2111.24702880055, 1.80568608554533, 4125.33471449469)
cow 23.8006362877428 (473.681844977678, -9128.2631528842, 239.033443917267, 18220.9025116129)
cow 32.4949977552751 (138.42400573552, -18035.3928847779, 387.916661080797, 36465.6768464101)
person 8.77725236878837 (502.371345131116, 210.051040886286, 42.5160061576193, 20.3697629750902)
diningtable 4.05980503916737 (-2256.01118752883, 343.210714264253, 5658.08545452834, 55.0627622079917)
tvmonitor 10.4713313523678 (-1818.03733710679, 194.896162589751, 4932.72937689972, 177.472580818184)
tvmonitor 12.2691757717801 (-18778.2914532181, 128.871683567611, 37271.2059504048, 29.0849889945601)
pottedplant 14.22723577094 (-28173.1173244905, -208.326447582406, 56501.6760140867, 546.753047487035)
tvmonitor 13.8996401643171 (-11994.0013712802, 370.284081045691, 24092.6758117422, 19.6677094960988)
bottle 24.2488610989835 (-195.202285100031, 385.567696758806, 1132.05653811156, 12.4024115628028)
person 17.197114360953 (-451.579067209645, -418.735144345297, 0.0300114537946303, 1134.27591314155)

Any help you can provide is greatly appreciated! Thanks! 👍

compile error！

Cannot build with my own frozen pb file

Hey, @KleinYuan .
Nice work on it :D 👍

There are a couple of things that's sort of an issue to me.

I installed the tensorflow on Ubuntu 16.04 and trained my own model
referring https://github.com/tensorflow/models/blob/master/object_detection/g3doc/running_locally.md on this.

After that, expert it to pb file,
and replace your 'tiny-yolo-voc.pb', 'vod.txt' with my own 'frozen_inference_graph.pb', 'txt' file.

but i've got error below ...
= = = = = = = = = = = = = = = = = = = =
/tensorflow-yolo-ios/tensorflow-yolo-ios/tensorflow-yolo-ios/tfWrap.mm:72] Loading model with memory mapped
2017-08-26 02:09:12.685433: E /tensorflow-yolo-ios/tensorflow-yolo-ios/tensorflow-yolo-ios/tensorflow_utils.mm:185] MMap failed with Corrupted memmapped model file: /var/containers/Bundle/Application/F9C63DBE-8A8B-44DB-B131-8C36A8B6F0DA/tensorflow-yolo-ios.app/frozen_inference_graph.pb Invalid directory offset
2017-08-26 02:09:12.685496: I /tensorflow-yolo-ios/tensorflow-yolo-ios/tensorflow-yolo-ios/tfWrap.mm:75] Loaded model with memory mapped
2017-08-26 02:09:12.685523: F /tensorflow-yolo-ios/tensorflow-yolo-ios/tensorflow-yolo-ios/tfWrap.mm:82] Couldn't load model: Data loss: Corrupted memmapped model file: /var/containers/Bundle/Application/F9C63DBE-8A8B-44DB-B131-8C36A8B6F0DA/tensorflow-yolo-ios.app/frozen_inference_graph.pb Invalid directory offset
= = = = = = = = = = = = = = = = = = = =

Actually, this model is working properly on Android object detection app.
so i'm really confused.

Is there any tips to generate own pb model for this project ?

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