here I want to train a object detection model, so I have annotated the data using roboflow and then exported it as tfrecords and also got the (.pbtxt file) and after that I don't have any clue on how to train a can model from scratch with just 2,3 number of hidden layers. am not getting on how to use that tfrecord to fit in my model which I have created. please help me out.
tfrecord files are usually used with Tensorflow Object Detection. It's pretty old and I haven't seen it used in practice recently, but there's a Tensorflow Object Detection tutorial here that uses these tfrecord files.
If there's not a particular reason you need to use TF Object Detection I'd recommend using a newer and more well-supported model like YOLOv5 or YOLOv7.
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I'm kinda new to TensorFlow and Keras, so please excuse any accidental stupidity, but I have an issue. I've been trying to load in models from the TensorFlow Detection Zoo, but haven't had much success.
I can't figure out how to read these saved_model folders (they contain a saved_model.pb file, and an assets and variables folder), so that they're accepted by Keras. Nor can I figure out a way to convert these models so that they may be loaded in. I've tried converting the SavedModel to ONNX, and then convert the ONNX-model to Keras, but that didn't work. Trying to load the original model as a saved_model, and then trying to to save this loaded model in another format gave me no success either.
Since you are new to Tensorflow (and I guess deep learning) I would suggest you stick with the API because the detection zoo models best interface with the object detection API. If you have already downloaded the model, you just need to export it using the exporter_main_v2.py script. This article explains it very well link.
I am trying to understand what I need from any pre-trained model used in the API regardless of any additional code found on the Tensorflow object detection API.
For example: ssd_mobilenet_v1_coco_2017_11_17, depending on what I have understood: it is a model that is already trained to detect objects (there is a classification to know the category of the object + Regression to bound the objects with rectangles and those rectangles are actually the x,y,w,h coordinates on the object).
How do we benefit from the regression output of that model (x,y,w,h coordinates) to use them in another model?
Let's assume we want to print out just the coordinates x,y,w,h of a detected object on an image without any need of the code of Tensorflow object detection API, how can we do that?
Certainly you can use the pretrained model provided in tensorflow object detection model zoo without installing object detection api. The alternative solution is to use opencv.
Opencv has provided both c++ and python api to call .pb models generated by tensorflow. Here is a nice tutorial.
I am building a new tensorflow model based off of SSD V1 coco model in order to perform real time object detection in a video but i m trying to find if there is a way to build a model where I can add a new class to the existing model so that my model has all those 90 classes available in SSD MOBILENET COCO v1 model and also contains the new classes that i want to classify.
For example, I have created training data for two classes: man, woman
Now, I built a new tensorflow model that identifies a man and/or woman in a video. However, my model does not have the other 90 classes present in original SSD Mobilenet model. I am looking for a way to concatenate both models or pass more than one model to my code to detect the objects.
If you have any questions or if I am not clear, please feel free to probe me further.
The only way i find is you need to get dataset of SSD Mobilenet model on which it was trained.
Make sure all the images are present in one directory and annotations in another directory.
We should have a corresponding annotation file for each image file
ex: myimage.jpg and myimage.xml
If all the images of your customed dataset are of same formate with SSD Mobilenet model then annotate it with a tool called LabelImg.
Add that images and annotated files to respective images and annotations directory where we have already saved SSD Mobilenet.
Try regenerate new TFrecord and continue with remaining procedure on it.
You can use transfer learning with Tensorflow API.
Transfer learning allows you to load re-trained network and modify the fully connected layer by introducing your classes.
There is full description for this in the following references:
Codelab
A good explanation here
Tensorflow API here for more details
Also you can use google cloud platform for better and faster results:
I wish this helps you.
I don't think there is a way you can add your classes to the existing 90 classes without using the dataset it is previously trained with. Your only way is to use that dataset plus your own and retrain the model.
Basically I am trying to use google's pre trained Speaker-id model for speaker detection. But this being a TensorFlow Lite model, I can't use it on my Linux pc. For that, I am trying to find a converter back to its frozen graph model.
Any help on this converter or any direct way to use tensorflow Lite pretrained models on desktop itself, will be appreciated.
You can use the converter which generates tflite models to convert it back to a .pb file if that is what you're searching for.
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
I have used the TensorFlow object detection API to train the SSD Inception model from scratch. The evaluation script shows that the model has learned something and now I want to use the model.
I have looked at the object detection ipynb that can feed single images to a trained model. However, this is for SSD with MobileNet. I have used the following line (after loading the meta graph) to print the tensor names of the TensorFlow model I trained.
print([str(op.name) for op in tf.get_default_graph().get_operations()] )
But it does not contain the same input or output tensor names as in the ipynb. I have also searched through the code, but many functions point toward each other and it is difficult to find what I am looking for.
How can I find the tensor names I need? Or is there another method I do not know about?
To use the graph, you need to freeze/export it, using this provided script. The resulting .pb file will contain the nodes you need. I don't know why it's organized like that, but it is.