voxl-tflite-server: "FATAL: Unsupported model provided!!"
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I am trying to create a custom version of the voxl-tflite-server using a tflite file of the literature version of the yolo-v4-tiny model. However, when I change the code as well as the the voxl-tflite-server.conf file to include my new model I get the error "FATAL: Unsupported model provided!!" when trying to run the server. I can't find where this message is printed in the code. How can I turn this error off?
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This error is printed at line 78 of threads.cpp. If you would like to run your own model, it is much simpler to just change the
tflite_settings->model_nameand
tflite_settings->labels_file_nameparameters in models.cpp to match the absolute paths on voxl where these files are located (typically /usr/bin/dnn/ or in the /data/ partition if the file is larger).
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@Matt-Turi thanks Matt. I ended up getting a segfault with it. I believe it was too memory intensive because it wasn't supported by the GPU. Is there a chance that tiny-yolo could be supported by ModalAI anytime soon?
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We've done some testing with darknet and a few tflite converted yolo models, but saw extremely poor inference times and performance from both. Is there any reason you are looking to use the yolo architecture specifically over mobilenet or some other native tensorflow model?
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I have some yolo weights on some custom data that I collected for yolo that I wanted to test with voxl-tflite-server. Additionally, I noticed that in there are some classes missing in the coco_labels.txt file included with the code for voxl-tflite-server in master. Some classes such as 'desk' are replaced with question marks:
0 person 1 bicycle 2 car 3 motorcycle 4 airplane 5 bus 6 train 7 truck 8 boat 9 traffic light 10 fire hydrant 11 ???-11 12 stop sign 13 parking meter 14 bench 15 bird 16 cat 17 dog 18 horse 19 sheep 20 cow 21 elephant 22 bear 23 zebra 24 giraffe 25 ??? 26 backpack 27 umbrella 28 ???-28 29 ???-29 30 handbag 31 tie 32 suitcase 33 frisbee 34 skis 35 snowboard 36 sports ball 37 kite 38 baseball bat 39 baseball glove 40 skateboard 41 surfboard 42 tennis racket 43 bottle 44 ???-44 45 wine glass 46 cup 47 fork 48 knife 49 spoon 50 bowl 51 banana 52 apple 53 sandwich 54 orange 55 broccoli 56 carrot 57 hot dog 58 pizza 59 donut 60 cake 61 chair 62 couch 63 potted plant 64 bed 65 ???-65 66 dining table 67 ???-67 68 ???-68 69 toilet 70 ???-70 71 tv 72 laptop 73 mouse 74 remote 75 keyboard 76 cell phone 77 microwave 78 oven 79 toaster 80 sink 81 refrigerator 82 ???-82 83 book 84 clock 85 vase 86 scissors 87 teddy bear 88 hair drier 89 toothbrush -
@Matt-Turi did you try the full fledged version of yolo or was it tiny-yolo?
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@colerose Looks like that labels file is a bit outdated - I will see to updating it.
For your other question - in the past, I tried with regular and tiny yolo implementations. With a modified darknet framework (opencl backend instead of cuda), the inference times for tiny-yolo were around 30 seconds per image, and full yolo was upwards of a minute per image. I also tested with the tflite-converted models, but saw similar performance results as well as various inconsistencies / unsupported ops due to the conversion (likely why you are getting segfaults with your model).
For reference, any of the object detection models from the tf1 zoo as well as a few from the tf2 zoo will integrate seamlessly and can support our gpu acceleration as seen with the mobilenetv2 model that is default.
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@Matt-Turi this is great to know, thanks Matt!
Just curious, any reason why mobilenetv2 was chosen from the tf1/tf2 zoo when it seems that there are faster and more accurate models available? -
@colerose we selected mobilenetv2 because of its exceptional performance on embedded devices - currently, inference time in the voxl-tflite-server is ~22ms per frame with very high precision. Also, the mobilenet family is fairly easy to retrain with a custom dataset!