Thanks for your good work. I have a customized model at two detection scale trained with four class.. How do I modify the code to fit the application? Moreso, how to set up --tfds_root for quantize.py?

I have convered yolov5s.pt to yolov5s_fp32_640.tflite, but I can't deploy it on android mobile phone. The version of yolov5s.pt is 6.1.

step1: python export.py --weights weights/yolov5s.pt --include onnx --simplify (the image_size of export.py is [640,640]). step2: python3 /opt/intel/openvino_2021.4.752/deployment_tools/model_optimizer/mo.py --input_model yolov5s.onnx --input_shape [1,3,640,640] --output_dir ./openvino --data_type FP32 --output Conv_198,Conv_248,Conv_298 step3: openvino2tensorflow --model_path ./openvino/yolov5s.xml --model_output_path tflite --output_pb --output_saved_model --output_no_quant_float32_tflite step4: rename file as "yolov5s_fp32_640.tflite", and put it into android/app/src/main/assets

but it can't run. “Cannot copy from a TensorFlowLite tensor (Identity) with shape [1,20,20,255] to a java object with shape [1,80,80,255]”

I managed to build my app and import a model but when I press RUN INFERENCE I get:

Inference failed : Cannot copy from a TensorFlowLite tensor (StatufulPartitionedCall:2) with shape [1, 20, 20, 51] to a Java object with shape [1, 40 , 40, 51]

Sir, I use this warehouse to build yolov5 lite, but I get the following error. The code is too long, I don’t know where to change it. Can you give some suggestions for me？ Thankyou very much！ The tflite model as follow： https://github.com/PINTO0309/openvino2tensorflow/files/7573864/saved_model.zip

I run command in colab: !python3 quantize.py --image_dir /contnet/dataset/coco128/coco128/images/train2017/ --calib_num 93 --input_size 640

the folder contains *.jpg & *.txt

Hi!

I tried my own tflite model trained with yolov5 on your app, but get the flow error:

Cannot copy from a TensorFlowLite tensor (StatefulPartitionedCall:0) with shape [1, 6300, 7] to a Java object with shape [1, 40, 40, 255].

your models have 4 outputs, but my models only have 3 outputs, what should i do to make my model work.

Thanks!

when i run python3 /opt/intel/openvino_2021.3.394/deployment_tools/model_optimizer/mo.py --input_model yolov5s.onnx --input_shape [1,3,640,640] --output_dir ./openvino --data_type FP32 --output Conv_253,Conv_302,Conv_351

it seems that "-output" is unnecessary

How to change TfliteRunner for working with custom Activity in manifest. I'am using MAppication class for starting app. MainActivity open in MApplication

I tried to run this project on the Android tablet, and found that the default horizontal screen was inaccurate. Then I switched back to the phone and rotated it to the horizontal screen, and the same inaccuracy occurred. I wonder if there is any good way to support horizontal screen detection

I have an interesting inference output. The image below is using conf=0.84 and IoU=0.30 First, the bboxes are smaller than the objects. Second, somehow when I have more than one bbox to the same object as shown in the image for bbox yellow and blue, note that in the yellow case, the higher prediction 93.82 the bbox is smaller than the 89.70 that has a bigger bbox. The same happens with blue case where the higher prediction 92.96 the bbox is smaller than the 91.48 has a bigger bbox. If I decrease IoU or increase the confidence, I will get only one bbox for yellow and blue with higher conf but the ones removed had a better bbox size when compared with their objects. Why is the algorithm assigning lower conf to bboxes with a larger size (near the object size) and higher conf to bboxes with smaller sizes? Any hint on this?