Object-Detection | android application which shows how a trained TensorFlow | Android library

While running models/research/object_detection/model_main_tf2.py from tensorflow/models (or just python -c "from object_detection import model_lib_v2") I get:

Summary/TL;DR: ML Object Detection application is unable to detect objects because images aren't being acquired by analyze() method.

I'm currently working on a mobile application using CameraX and Google ML Kit written in Java. The purpose of the application is to detect objects with a real time camera preview. I implemented ML Kit using this guide aptly titled "Detect and track objects with ML Kit on Android" (base model option) to detect objects in successive frames within the application.

However, upon running the application, it launches on my device and the camera preview continues to work but the application does not carry out its intended effect of actually detecting objects and displaying it on my screen. To attempt to resolve this, I had found this StackOverflow answer that very closely resembles this issue. To my dismay, the user had built their application using a custom model (tflite). This differs from mine as I am using the base model. According to my research, this uses the ML Kit's on-device's object detection. The code being applied is restricted to what is present within the aforementioned documentation. Since my IDE (Android Studio) does not display any errors within the syntax, I am unsure as to why any object detection does not appear to be present on my application. Displayed below is the necessary code that had been used:

I followed the "Training Custom Object Detector" tutorial (https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html)

When running the script to continue training a pre-trained model:

python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config

(found here: https://github.com/tensorflow/models/blob/master/research/object_detection/model_main_tf2.py)

Using different numpy versions, I get the following errors.

Scenario #1:

• tensorflow: 2.2.0
• numpy: 1.20.0-1

NotImplementedError: Cannot convert a symbolic Tensor (cond_2/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported

I looked online and it suggests to downgrade the numpy version < 1.20.0 (NotImplementedError: Cannot convert a symbolic Tensor (2nd_target:0) to a numpy array). Note, the version numpy version must be >= 1.19.2 for tensorflow 2.2.0.

Scenario #2:

• tensorflow: 2.2.0
• numpy: 1.19.2-5

ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject

However, the online recommendation is to upgrade numpy to >= 1.20.0. (ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject). There is a github issue related to this: https://github.com/tensorflow/models/issues/9749.

I'm not sure what version to use to get the code running.

Appendix: Scenario #1 Error Code (numpy: 1.20.0-1)

I ran through the TensorFlow object detection installation on Windows.

https://medium.com/riow/tensorflow-object-detection-on-windows-ad10bfde217c

After successfully installing TensorFlow object detection,

I ran following command to test, and receiving error below. How can this be fixed?

I am trying to install Tensor Flow Object Detection on Windows 10.

Resource: https://towardsdatascience.com/how-to-install-tensorflow-2-object-detection-api-on-windows-2eef9b7ae869

After running these steps, we are receiving the errors below. It is stalling on pyarrow. How can this be fixed?

I try to train a model object detection and I follow this tutorial: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/tensorflow-1.14/training.html

But at the end I execute the command in the cmd : python model_main.py --alsologtostderr --model_dir=training/ --pipeline_config_path=training/ssd_inception_v2_coco.config

and it return the following lines:

I am attempting to train an object detection model using Tensorflow's Object Detection API 2 and Tensorflow 2.3.0. I have largely been using this article as a resource in preparing the data and training the model.

Most articles which use the Object Detection API download a pre-trained model from the Tensorflow model zoo prior to fine-tuning.

The Tensorflow Model Zoo is a set of links on a Github page set up by the Object Detection team. When I click one such link (using Google Chrome), a new tab opens briefly as if a download is starting, then immediately closes and a download does not occur. Hyperlinks to other models I have found in articles also have not worked.

To anyone who has worked with fine-tuning using the Object Detection API: What method did you use to download a pre-trained model? Did the model zoo links work? If not, what resource did you use instead?

Any help is much appreciated.

I am running confusion matrix on my own custom model using Tensorflow Object Detection API. I am using Faster R-CNN Inception v2 pets. I get this output:

Based on what is written on the TensorFlow web page about object-detection API, I have trained a custom model, and its checkpoint is obtained successfully, now I am going to apply this trained model on a pre-captured video(not webcam frames). Now, I would like to know how can I do this? is there any pre-written code that can help me in this way? thanks for your comments. best wishes to you.