How to configure Tensorflow object detection Android demo to work with Inception v2
We have built an Android app based on the Tensorflow object detection Android demo app. It works when using a Mobilenet network, but crashes if we try to use an Inception v2 based network.
It is possible for Tensorflow Inception v2 object detection to work on Android?
https://github.com/tensorflow/models/tree/master/research/object_detection
We are using the exact same code as the Tensorflow detector demo here.
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java
Models from zoo here,
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
Using TF Object Detection API.
If we use a network pb file trained using ssd_mobilenet_v2_coco the demo app works.
If we use a network pb file trained using faster_rcnn_inception_v2_coco it crashes, (see below)
Is it possible for the Android app to work with the Inception v2 model?
(the mobile-net accuracy is very bad, but Inception is much better)
11-14 12:11:47.817 7122-7199/org.tensorflow.demo E/AndroidRuntime: FATAL EXCEPTION: inference
Process: org.tensorflow.demo, PID: 7122
java.nio.BufferOverflowException
at java.nio.FloatBuffer.put(FloatBuffer.java:444)
at org.tensorflow.Tensor.writeTo(Tensor.java:488)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.fetch(TensorFlowInferenceInterface.java:488)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.fetch(TensorFlowInferenceInterface.java:442)
at org.tensorflow.demo.TensorFlowObjectDetectionAPIModel.recognizeImage(TensorFlowObjectDetectionAPIModel.java:170)
at org.tensorflow.demo.DetectorActivity$3.run(DetectorActivity.java:288)
at android.os.Handler.handleCallback(Handler.java:739)
at android.os.Handler.dispatchMessage(Handler.java:95)
at android.os.Looper.loop(Looper.java:148)
at android.os.HandlerThread.run(HandlerThread.java:61)
android tensorflow object-detection object-detection-api asked Nov 8 '18 at 21:10
JamesJames
20.8k85499
add a comment |
We have built an Android app based on the Tensorflow object detection Android demo app. It works when using a Mobilenet network, but crashes if we try to use an Inception v2 based network.
It is possible for Tensorflow Inception v2 object detection to work on Android?
https://github.com/tensorflow/models/tree/master/research/object_detection
We are using the exact same code as the Tensorflow detector demo here.
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java
Models from zoo here,
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
Using TF Object Detection API.
If we use a network pb file trained using ssd_mobilenet_v2_coco the demo app works.
If we use a network pb file trained using faster_rcnn_inception_v2_coco it crashes, (see below)
Is it possible for the Android app to work with the Inception v2 model?
(the mobile-net accuracy is very bad, but Inception is much better)
11-14 12:11:47.817 7122-7199/org.tensorflow.demo E/AndroidRuntime: FATAL EXCEPTION: inference
Process: org.tensorflow.demo, PID: 7122
java.nio.BufferOverflowException
at java.nio.FloatBuffer.put(FloatBuffer.java:444)
at org.tensorflow.Tensor.writeTo(Tensor.java:488)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.fetch(TensorFlowInferenceInterface.java:488)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.fetch(TensorFlowInferenceInterface.java:442)
at org.tensorflow.demo.TensorFlowObjectDetectionAPIModel.recognizeImage(TensorFlowObjectDetectionAPIModel.java:170)
at org.tensorflow.demo.DetectorActivity$3.run(DetectorActivity.java:288)
at android.os.Handler.handleCallback(Handler.java:739)
at android.os.Handler.dispatchMessage(Handler.java:95)
at android.os.Looper.loop(Looper.java:148)
at android.os.HandlerThread.run(HandlerThread.java:61)
asked Nov 8 '18 at 21:10
JamesJames
20.8k85499
Please provide more information about problem itself? Why the app crashes? Is there any ouptut here? What framework did you us? TFmobile or TFLite? Like @Derek said, it should be pssoble to use inception on mobile in theory.
– Janikan
Nov 12 '18 at 21:34
Updated post with details and stack.
– James
Nov 14 '18 at 20:05
You need to limit the maximum number of detections per image or it will hog the memory and you will end up with overflow errors
– mlRocks
Nov 16 '18 at 6:59
add a comment |
We have built an Android app based on the Tensorflow object detection Android demo app. It works when using a Mobilenet network, but crashes if we try to use an Inception v2 based network.
It is possible for Tensorflow Inception v2 object detection to work on Android?
https://github.com/tensorflow/models/tree/master/research/object_detection
We are using the exact same code as the Tensorflow detector demo here.
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java
Models from zoo here,
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
Using TF Object Detection API.
If we use a network pb file trained using ssd_mobilenet_v2_coco the demo app works.
If we use a network pb file trained using faster_rcnn_inception_v2_coco it crashes, (see below)
Is it possible for the Android app to work with the Inception v2 model?
(the mobile-net accuracy is very bad, but Inception is much better)
11-14 12:11:47.817 7122-7199/org.tensorflow.demo E/AndroidRuntime: FATAL EXCEPTION: inference
Process: org.tensorflow.demo, PID: 7122
java.nio.BufferOverflowException
at java.nio.FloatBuffer.put(FloatBuffer.java:444)
at org.tensorflow.Tensor.writeTo(Tensor.java:488)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.fetch(TensorFlowInferenceInterface.java:488)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.fetch(TensorFlowInferenceInterface.java:442)
at org.tensorflow.demo.TensorFlowObjectDetectionAPIModel.recognizeImage(TensorFlowObjectDetectionAPIModel.java:170)
at org.tensorflow.demo.DetectorActivity$3.run(DetectorActivity.java:288)
at android.os.Handler.handleCallback(Handler.java:739)
at android.os.Handler.dispatchMessage(Handler.java:95)
at android.os.Looper.loop(Looper.java:148)
at android.os.HandlerThread.run(HandlerThread.java:61)
asked Nov 8 '18 at 21:10
JamesJames
20.8k85499
We have built an Android app based on the Tensorflow object detection Android demo app. It works when using a Mobilenet network, but crashes if we try to use an Inception v2 based network.
It is possible for Tensorflow Inception v2 object detection to work on Android?
https://github.com/tensorflow/models/tree/master/research/object_detection
We are using the exact same code as the Tensorflow detector demo here.
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java
Models from zoo here,
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
Using TF Object Detection API.
If we use a network pb file trained using ssd_mobilenet_v2_coco the demo app works.
If we use a network pb file trained using faster_rcnn_inception_v2_coco it crashes, (see below)
Is it possible for the Android app to work with the Inception v2 model?
(the mobile-net accuracy is very bad, but Inception is much better)
11-14 12:11:47.817 7122-7199/org.tensorflow.demo E/AndroidRuntime: FATAL EXCEPTION: inference
Process: org.tensorflow.demo, PID: 7122
java.nio.BufferOverflowException
at java.nio.FloatBuffer.put(FloatBuffer.java:444)
at org.tensorflow.Tensor.writeTo(Tensor.java:488)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.fetch(TensorFlowInferenceInterface.java:488)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.fetch(TensorFlowInferenceInterface.java:442)
at org.tensorflow.demo.TensorFlowObjectDetectionAPIModel.recognizeImage(TensorFlowObjectDetectionAPIModel.java:170)
at org.tensorflow.demo.DetectorActivity$3.run(DetectorActivity.java:288)
at android.os.Handler.handleCallback(Handler.java:739)
at android.os.Handler.dispatchMessage(Handler.java:95)
at android.os.Looper.loop(Looper.java:148)
at android.os.HandlerThread.run(HandlerThread.java:61)
asked Nov 8 '18 at 21:10
JamesJames
20.8k85499
asked Nov 8 '18 at 21:10
JamesJames
20.8k85499
edited Nov 14 '18 at 20:03
James
asked Nov 8 '18 at 21:10
JamesJames
20.8k85499
asked Nov 8 '18 at 21:10
JamesJames
20.8k85499
asked Nov 8 '18 at 21:10
JamesJames
20.8k85499
20.8k85499
Please provide more information about problem itself? Why the app crashes? Is there any ouptut here? What framework did you us? TFmobile or TFLite? Like @Derek said, it should be pssoble to use inception on mobile in theory.
– Janikan
Nov 12 '18 at 21:34
Updated post with details and stack.
– James
Nov 14 '18 at 20:05
You need to limit the maximum number of detections per image or it will hog the memory and you will end up with overflow errors
– mlRocks
Nov 16 '18 at 6:59
add a comment |
Please provide more information about problem itself? Why the app crashes? Is there any ouptut here? What framework did you us? TFmobile or TFLite? Like @Derek said, it should be pssoble to use inception on mobile in theory.
– Janikan
Nov 12 '18 at 21:34
Updated post with details and stack.
– James
Nov 14 '18 at 20:05
You need to limit the maximum number of detections per image or it will hog the memory and you will end up with overflow errors
– mlRocks
Nov 16 '18 at 6:59
Please provide more information about problem itself? Why the app crashes? Is there any ouptut here? What framework did you us? TFmobile or TFLite? Like @Derek said, it should be pssoble to use inception on mobile in theory.
– Janikan
Nov 12 '18 at 21:34
Please provide more information about problem itself? Why the app crashes? Is there any ouptut here? What framework did you us? TFmobile or TFLite? Like @Derek said, it should be pssoble to use inception on mobile in theory.
– Janikan
Nov 12 '18 at 21:34
Updated post with details and stack.
– James
Nov 14 '18 at 20:05
Updated post with details and stack.
– James
Nov 14 '18 at 20:05
You need to limit the maximum number of detections per image or it will hog the memory and you will end up with overflow errors
– mlRocks
Nov 16 '18 at 6:59
You need to limit the maximum number of detections per image or it will hog the memory and you will end up with overflow errors
– mlRocks
Nov 16 '18 at 6:59
add a comment |
2 Answers
2
active
oldest
votes
I read about this issue once.
I think the problem is in this line of your code:
private static final int MAX_RESULTS = 100;
This creates an array for output with the specified length. I think SSD mobilenet gives this number of predictions at maximum, but default faster RCNN (without any configurations from your side) gives you more. Try to increase this value for example to 500.
answered Nov 15 '18 at 8:42
JanikanJanikan
2427
Thanks, this fixes the issue. It works now (but is too slow to be usable, so back to mobilenet)
– James
Nov 19 '18 at 14:34
add a comment |
It should be possible to use SSD Inception, although not advisable. Inception is quite large for mobile, and I don't believe we don't have quantization support for it right now.
answered Nov 12 '18 at 17:42
Derek ChowDerek Chow
59416
So do you mean faster_rcnn_inception_v2_coco will not work, but you think ssd_inception_v2_coco will work?
– James
Nov 14 '18 at 20:04
I think this is untested, but I have no reason to believe it doesn't work. So long as the Tensorflow graph rewriter that adds the FakeQuant nodes works, it will train with range support for quantization.
– Derek Chow
Nov 16 '18 at 1:18
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
I read about this issue once.
I think the problem is in this line of your code:
private static final int MAX_RESULTS = 100;
This creates an array for output with the specified length. I think SSD mobilenet gives this number of predictions at maximum, but default faster RCNN (without any configurations from your side) gives you more. Try to increase this value for example to 500.
answered Nov 15 '18 at 8:42
JanikanJanikan
2427
Thanks, this fixes the issue. It works now (but is too slow to be usable, so back to mobilenet)
– James
Nov 19 '18 at 14:34
add a comment |
I read about this issue once.
I think the problem is in this line of your code:
private static final int MAX_RESULTS = 100;
This creates an array for output with the specified length. I think SSD mobilenet gives this number of predictions at maximum, but default faster RCNN (without any configurations from your side) gives you more. Try to increase this value for example to 500.
answered Nov 15 '18 at 8:42
JanikanJanikan
2427
Thanks, this fixes the issue. It works now (but is too slow to be usable, so back to mobilenet)
– James
Nov 19 '18 at 14:34
add a comment |
I read about this issue once.
I think the problem is in this line of your code:
private static final int MAX_RESULTS = 100;
This creates an array for output with the specified length. I think SSD mobilenet gives this number of predictions at maximum, but default faster RCNN (without any configurations from your side) gives you more. Try to increase this value for example to 500.
answered Nov 15 '18 at 8:42
JanikanJanikan
2427
I read about this issue once.
I think the problem is in this line of your code:
private static final int MAX_RESULTS = 100;
This creates an array for output with the specified length. I think SSD mobilenet gives this number of predictions at maximum, but default faster RCNN (without any configurations from your side) gives you more. Try to increase this value for example to 500.
answered Nov 15 '18 at 8:42
JanikanJanikan
2427
answered Nov 15 '18 at 8:42
JanikanJanikan
2427
answered Nov 15 '18 at 8:42
JanikanJanikan
2427
answered Nov 15 '18 at 8:42
JanikanJanikan
2427
2427
Thanks, this fixes the issue. It works now (but is too slow to be usable, so back to mobilenet)
– James
Nov 19 '18 at 14:34
add a comment |
Thanks, this fixes the issue. It works now (but is too slow to be usable, so back to mobilenet)
– James
Nov 19 '18 at 14:34
Thanks, this fixes the issue. It works now (but is too slow to be usable, so back to mobilenet)
– James
Nov 19 '18 at 14:34
Thanks, this fixes the issue. It works now (but is too slow to be usable, so back to mobilenet)
– James
Nov 19 '18 at 14:34
add a comment |
It should be possible to use SSD Inception, although not advisable. Inception is quite large for mobile, and I don't believe we don't have quantization support for it right now.
answered Nov 12 '18 at 17:42
Derek ChowDerek Chow
59416
So do you mean faster_rcnn_inception_v2_coco will not work, but you think ssd_inception_v2_coco will work?
– James
Nov 14 '18 at 20:04
I think this is untested, but I have no reason to believe it doesn't work. So long as the Tensorflow graph rewriter that adds the FakeQuant nodes works, it will train with range support for quantization.
– Derek Chow
Nov 16 '18 at 1:18
add a comment |
It should be possible to use SSD Inception, although not advisable. Inception is quite large for mobile, and I don't believe we don't have quantization support for it right now.
answered Nov 12 '18 at 17:42
Derek ChowDerek Chow
59416
So do you mean faster_rcnn_inception_v2_coco will not work, but you think ssd_inception_v2_coco will work?
– James
Nov 14 '18 at 20:04
I think this is untested, but I have no reason to believe it doesn't work. So long as the Tensorflow graph rewriter that adds the FakeQuant nodes works, it will train with range support for quantization.
– Derek Chow
Nov 16 '18 at 1:18
add a comment |
It should be possible to use SSD Inception, although not advisable. Inception is quite large for mobile, and I don't believe we don't have quantization support for it right now.
answered Nov 12 '18 at 17:42
Derek ChowDerek Chow
59416
It should be possible to use SSD Inception, although not advisable. Inception is quite large for mobile, and I don't believe we don't have quantization support for it right now.
answered Nov 12 '18 at 17:42
Derek ChowDerek Chow
59416
answered Nov 12 '18 at 17:42
Derek ChowDerek Chow
59416
answered Nov 12 '18 at 17:42
Derek ChowDerek Chow
59416
answered Nov 12 '18 at 17:42
Derek ChowDerek Chow
59416
59416
So do you mean faster_rcnn_inception_v2_coco will not work, but you think ssd_inception_v2_coco will work?
– James
Nov 14 '18 at 20:04
I think this is untested, but I have no reason to believe it doesn't work. So long as the Tensorflow graph rewriter that adds the FakeQuant nodes works, it will train with range support for quantization.
– Derek Chow
Nov 16 '18 at 1:18
add a comment |
So do you mean faster_rcnn_inception_v2_coco will not work, but you think ssd_inception_v2_coco will work?
– James
Nov 14 '18 at 20:04
I think this is untested, but I have no reason to believe it doesn't work. So long as the Tensorflow graph rewriter that adds the FakeQuant nodes works, it will train with range support for quantization.
– Derek Chow
Nov 16 '18 at 1:18
So do you mean faster_rcnn_inception_v2_coco will not work, but you think ssd_inception_v2_coco will work?
– James
Nov 14 '18 at 20:04
So do you mean faster_rcnn_inception_v2_coco will not work, but you think ssd_inception_v2_coco will work?
– James
Nov 14 '18 at 20:04
I think this is untested, but I have no reason to believe it doesn't work. So long as the Tensorflow graph rewriter that adds the FakeQuant nodes works, it will train with range support for quantization.
– Derek Chow
Nov 16 '18 at 1:18
I think this is untested, but I have no reason to believe it doesn't work. So long as the Tensorflow graph rewriter that adds the FakeQuant nodes works, it will train with range support for quantization.
– Derek Chow
Nov 16 '18 at 1:18
add a comment |
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Please provide more information about problem itself? Why the app crashes? Is there any ouptut here? What framework did you us? TFmobile or TFLite? Like @Derek said, it should be pssoble to use inception on mobile in theory.
– Janikan
Nov 12 '18 at 21:34
Updated post with details and stack.
– James
Nov 14 '18 at 20:05
You need to limit the maximum number of detections per image or it will hog the memory and you will end up with overflow errors
– mlRocks
Nov 16 '18 at 6:59