Does anyone know the original source code of the tensorflow_inception_graph.pb.
I really wan't to know the operation in the example project
The tensorflow/example/android - Read me.
Tensorflow Android Camera Demo
https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/android/
https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
Thanks
This code has not yet been released. As the release announcement for the latest ImageNet model mentions, the team is working on releasing the complete Python training framework for this type of model, including the code for building the graph.
I think you the rb and inception labels assets directly from here.
Related
I am currently trying to train a custom model for use in Unity (Barracuda) for object detection and I am struggling near what I believe to be the last part of the pipeline. Following various tutorials and git-repos I have done the following...
Using Darknet, I have trained a custom-model using the Tiny-Yolov2 model. (model tested successfully on a webcam python script)
I have taken the final weights from that training and converted them
to a Keras (h5) file. (model tested successfully on a webcam python
script)
From Keras, I then use tf.save_model to turn it into a
save_model.pd.
From save_model.pd I then convert it using tf2onnx.convert to change
it to an onnx file.
Supposedly from there it can then work in one of a few Unity sample
projects...
...however, this project fails to read in the Unity Sample projects I've tried to use. From various posts it seems that I may need to use a 'frozen' save_model.pd before converting it to ONNX. However all the guides and python functions that seem to be used for freezing save_models require a lot more arguments than I have awareness of or data for after going through so many systems. https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py - for example, after converting into Keras, I only get left with a h5 file, with no knowledge of what an input_graph_def, or output_node_names might refer to.
Additionally, for whatever reason, I cannot find any TF version (1 or 2) that can successfully run this python script using 'from tensorflow.python.checkpoint import checkpoint_management' it genuinely seems like it not longer exists.
I am not sure why I am going through all of these conversions and steps but every attempt to find a cleaner process between training and unity seemed to lead only to dead ends.
Any help or guidance on this topic would be sincerely appreciated, thank you.
I spent some time already to figure out how to get Mask R-CNN working properly.
I cloned the original Matterport implementation and a fork of it which has been modified to use TF 2.
The Matterport implementation seems to be somehow outdated with respect to the dependencies, and I could not make it work. I saw that some people could make it work using different versions of the required libraries or some code changes here and there... I thought I continue with the TF2 compatible version. There is a code change needed as well to make it work with the examples which have been provided with Mask R-CNN. I hope that this is sufficient and that I did not miss something else.
E.g. I ran the train_shapes.ipynb in samples folder. The generated shapes are trained on top of pretrained COCO weights. So far so good.
The notebook generates a sample image with shapes, and processes it. this is the result:
What can be the reason that so many shapes are detected which are not in the source image?
I was having the same issue. It is because model.detect does not work for TensorFlow 2.6 and above. When I downgraded to TensorFlow 2.4, everything worked. Check out this thread: https://github.com/matterport/Mask_RCNN/issues/2670
Please i need you help concerning my yolov5 training process for object detection!
I try to train my object detection model yolov5 for detecting small object ( scratch). For labelling my images i used roboflow, where i applied some data augmentation and some pre-processing that roboflow offers as a services. when i finish the pre-processing step and the data augmentation roboflow gives the choice for different output format, in my case it is yolov5 pytorch, and roboflow does everything for me splitting the data into training validation and test. Hence, Everything was set up as it should be for my data preparation and i got at the end the folder with data.yaml and the images with its labels, in data.yaml i put the path of my training and validation sets as i saw in the GitHub tutorial for yolov5. I followed the steps very carefully tought.
The problem is when the training start i get nan in the obj and box column as you can see in the picture bellow, that i don't know the reason why, can someone relate to that or give me any clue to find the solution please, it's my first project in computer vision.
This is what i get when the training process starts
This the last message error when the training finish
I think the problem comes maybe from here but i don't know how to fix it, i used the code of yolov5 team as it's in the tuto
The training continue without any problem but the map and precision remains 0 all the process !!
Ps : Here is the link of tuto i followed : https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
This is what I would do to troubleshoot it. - Run your code on collab because the environment is proven to work well - Confirm that your labels look good and are setup correctly. Can you checked to ensure the classes look right? In one of the screenshots it looks like you have no labels
Running my code in colab worked successfully and the resulats were good. I think that the problem was in my personnel laptop environment maybe the version of pytorch i was using '1.10.0+cu113', or something else ! If you have any advices to set up my environnement for yolov5 properly i would be happy to take from you guys. many Thanks again to #alexheat
I'm using Yolov5 for my custom dataset too. This problem might be due to the directory misplacement.
And using different version of Pytorch will not be a problem. Anyway you can try using the version they mentioned in 'requirements.txt'
It's better if you run
cd yolov5
pip3 install -r requirements.txt
Let me know if this helps.
I am new here. I recently started working with object detection and decided to use the Tensorflow object detection API. But, when I start training the model, it does not display the global step like it should, although it's still training in the background.
Details:
I am training on a server and accessing it using OpenSSH on Windows. I trained a custom dataset, by collecting pictures and labeling them. I trained it using model_main.py. Also, until a couple of months back, the API was a little different, and only recently they changed to the latest version. For instance, earlier it used to use train.py for training, instead of model_main.py. All the online tutorial I can find use train.py, so it might be a problem with the latest commit. But I don't find anyone else fining this problem.
Thanks in advance!
Add tf.logging.set_verbosity(tf.logging.INFO) after the import section of the model_main.py script. It will display a summary after every 100th step.
As Thommy257 suggested, adding tf.logging.set_verbosity(tf.logging.INFO) after the import section of model_main.py prints the summary after every 100 steps by default.
Further, to specify the frequency of the summary, change
config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir)
to
config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir, log_step_count_steps=k)
where it will print after every k steps.
Regarding the recent change to model_main , the previous version is available at the folder "legacy". I use train.py and eval.py from this legacy folder with the same functionality as before.
I wanted to quantize (change all the floats into INT8) a ssd-mobilenet model and then want to deploy it onto my raspberry-pi. So far, I have not yet found any thing which can help me with it. Any help would be highly appreciated.
I saw tensorflow-lite but it seems it only supports android and iOS.
Any library/framweork is acceptable.
Thanks in advance.
Tensorflow Lite now has support for the Raspberry Pi via Makefiles. Here's the shell script. Regarding Mobilenet-SSD, you can get details on how to use it with TensorFlow Lite in this blog post (and here)
You can try using TensorRT library.
One of the features of the library is quantization.
In general mobilenets are difficult to quantize (see https://arxiv.org/pdf/2004.09602.pdf) but the library should do a good work