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Hello
I used mobilenet_ssd_v2 from tensorflow model zoo,
Its working fine for nearby object but for distant object there's offset in output
As you can check in following images
I used openvino ssd async example ....for getting following output
I used following commands to get model converted...
python mo_tf.py --input_model ../model_downloader/object_detection/common/ssd_mobilenet_v2_coco/tf/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb -o ../model_files/ssd_v2/ --tensorflow_use_custom_operations_config extensions/front/tf/ssd_v2_support.json --tensorflow_object_detection_api_pipeline_config ../model_downloader/object_detection/common/ssd_mobilenet_v2_coco/tf/ssd_mobilenet_v2_coco_2018_03_29/pipeline.config --reverse_input_channels
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Dear khandelwal, prateek
Were your model optimizer args like this ? Take a look at C:\Program Files (x86)\IntelSWTools\openvino_2019.2.275\deployment_tools\tools\model_downloader\list_topologies.yml and search for "ssd_mobilenet_v2" Looking at your MO command above it looks different from below. For instance you're not doing - --output=detection_classes,detection_scores,detection_boxes,num_detections
model_optimizer_args:
- --framework=tf
- --data_type=FP32
- --reverse_input_channels
- --input_shape=[1,300,300,3]
- --input=image_tensor
- --tensorflow_use_custom_operations_config=$mo_dir/extensions/front/tf/ssd_v2_support.json
- --tensorflow_object_detection_api_pipeline_config=$dl_dir/ssd_mobilenet_v2_coco_2018_03_29/pipeline.config
- --output=detection_classes,detection_scores,detection_boxes,num_detections
- --input_model=$dl_dir/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb
Hope it helps.
Thanks,
Shubha
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hello shubha..
I tried the command you share to convert model...
but still no change...
pls help..
python mo.py --framework=tf --data_type=FP32 --reverse_input_channels --input_shape=[1,300,300,3] --input=image_tensor --tensorflow_use_custom_operations_config=extensions/front/tf/ssd_v2_support.json --tensorflow_object_detection_api_pipeline_config=../model_files/ssd_v2/ssd_mobilenet_v2_coco_2018_03_29/pipeline.config --output=detection_classes,detection_scores,detection_boxes,num_detections --input_model=../model_files/ssd_v2/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb -o ../model_files/ssd_v2/
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Dear khandelwal, prateek,
Are you using OpenVino's SSD Demo to run inference ? We also have a Python version of SSD Async . If you are writing your own code from scratch please compare your code to OpenVino samples.
Thanks,
Shubha
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Hello shubha..
I used python example only..
The problems is with original model itself..
there's offset for far away objects...
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Dear khandelwal, prateek,
Yes it seems so. I think this may be a bug. Can you attach your photo to this ticket ? I will attempt to reproduce and get back to you on this forum.
Thanks !
Shubha
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hey shubha
what do you mean by photo?
you mean personal pic?
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Dear khandelwal, prateek
Yes, I mean that very photo you used to find the problem -
I have PM'd you so that you can share it privately.
Thanks,
Shubha
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Dear prateek,
I have PM'd you. But I need the actual *.jpg photo you used in this post (without the bounding boxes of course). Also I assume that you don't see these offsets using the mobilenet_ssd_v2 tensorflow model when you use Tensorflow for inference ? If you're sure that Tensorflow is accurate then this could be an OpenVino bug.
Thanks,
Shubha
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Hello shubha..
I said the same, in my last comment
the problem is there with original model itself...
i tested tensorflow model on nvidia gpu machine..
got similar offset..
offcourse..i didn;t compare result exactly..but ...there was visible offset in ssd mobilenet out of box itself
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Dear khandelwal, prateek,
OK I missed it. Then in this case it's not an OpenVino issue, since you're observing the same offset on an Nvidia GPU. It's a Tensorfow Model issue. Glad we sorted this out ! Unfortunately this forum is to address OpenVino questions, namely Model Optimizer and Inference Engine. Kindly consult the Tensorflow forums for help with Tensorflow specific issues.
Thanks !
Shubha
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