I am trying to use an object detection model, that was created using the TF Object Detection API, in TensorFlow.js.
I converted the model using:
tensorflowjs_converter --input_format=tf_saved_model inference_graph/saved_model inference_graph/web_model
It gets converted without any problems and loads in my javascript code.
Now I am a bit unsure about what my next steps should be.
I have to translate the Python into JavaScript, but certain areas I am unsure about.
With the object detection API in python, there are many steps; (1)preprocessing the image, such as convert to RGB, numpy array reshape, expand dimensions (I have an idea of how I would approach it) and (2) the run inference for single image function, I am not sure how I would go about it in tensorflow.js.
I tried to find some general information about deploying an object detection model in tensorflow.js, but I could not find much, except with pre-trained models.
Any information about this topic would be great!
Thanks!
as mentioned by #edkeveked you will need to perform similar input processing and output processing in JavaScript as is being done in Python. i can't say exactly what you will need to do since i am not familiar with the model. however, you can find an example using a specific object detection model here:
https://github.com/vabarbosa/tfjs-model-playground/blob/master/object-detector/demo/object-detector.js
see also
https://medium.com/codait/bring-machine-learning-to-the-browser-with-tensorflow-js-part-iii-62d2b09b10a3
You would need to replicate the same process in javascript before giving it to the model. In js, the image use by default the RGB channel, so there is no need to make that conversion again.
Related
I have a custom object detection model that I can call with model = MyModel() and model.loadweights(checkpoint) and I want to evaluate it using the Object Detection API.
From what I understood there are two possibilities, either I use the legacy eval.py, there I don't know, what to put into the pipeline_config file
Or I use the newer version that is implemented in model_main_tf2.py, but there I would have to save my model as model.config and I don't know what to put the pipeline file either.
Since my model is a YOLO model, it is not included in the sample once yet.
https://github.com/tensorflow/models/tree/master/research/object_detection/configs/tf2
Would really appreciate the help!
You can't calculate the mAP using the Object Detection API because there's no pipeline.config file for Yolo.
However, you can check this repo out. It's a Tensorflow based implementation of YoloV3. They have working code for calculating mAP. You can modify this accordingly to calculate the mAP of your model.
I'm fairly new to TensorFlow so I apologize if I'm saying something absurd.
I've been playing with the PoseNet model in the browser using TensorFlow.js. In this project, I can change the algorithm and parameters so I can get better results on the detection of certain poses. The most important params in my use case are the Multiplier, Quant Bytes and Output Stride.
So far so good, I have the results I want. However, I want to convert these results to TensorFlow Lite so I can use it in an iOS application. I managed to find the PoseNet model in a TensorFlow Lite file (tflite) and I even found an iOS app example provided by TensorFlow to I'm able to load up the model file and have it working on iOS.
The problem is...I'm unable to change the params (Multiplier, Quant Bytes and Output Stride) on the iOS app. I can't find it anywhere how I can do this. I've tried searching for these params in the iOS app source code, I've tried to find ways to convert a TensorFlow.js model to TensorFlow Lite so I can load the model with the params I want in the app but no luck.
I'm writing this post so maybe you guys can point me in the right direction so I'm able to "translate" what I have on TensorFlow.js to TensorFlow Lite.
EDIT:
This is what I've learned in the last couple of days:
TFLite is designed for serving fixed model with lightweight runtime. Thus, modifying model parameters on demand is not a design goal for it.
I looked at the TF.js code for PoseNet, and found similar design. It seems you can modify parameters, because they actually have different models for each params. https://github.com/tensorflow/tfjs-models/blob/b72c10bdbdec6b04a13f780180ed904736fa52a5/posenet/src/checkpoints.ts#L37
TFLite models generally don't support dynamic parameters. Output stride Multiplier and Quant Bytes are fixed params when the neural network is created.
So what I want to do is to extract weights from TF.js model, and put then into existing MobileNet code.
And that's where I need help now. Could anyone point me in the direction to load and change the model so I can then convert it to tflite with my own params?
EDIT2:
I found a repo that is helping me convert TF.js models to TF Lite Griffin98/posenet_tfjs2tflite. I still can't define the Quant Bytes tho.
Issue in short
In Python version of Tensorflow there is a tf.random.categorical() method that draws samples from a categorical distribution. But I can't find a similar method in TensorFlow.js API. So, what is the proper way to draw samples from a categorical distribution in TensorFlow.js?
Issue in details
In Text generation with an RNN tutorial the tf.random.categorical() method is being used in generate_text() function to decide what character should be passed next to the RNN input to generate a sequence.
predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
I'm experimenting with TensorFlow.js and trying trying to generate a "random" Shakespeare-like writing but in the browser. All parts of the tutorial seems to work well together except the step with using a tf.random.categorical() method.
I guess writing the alternative to tf.random.categorical() function manually should not be that hard, and also there are couple of 3rd-party JavaScript libraries that implement this functionality already, but it looks pretty logical to have it as a part of TensorFlow.js API.
I think you can use tf.multinomial instead.
I peeked at the source code and with name and seed parameters set to None, it is essentially the same as tf.multinomial with some random seeding going on, I guess.
I am very new to CNTK.
I wanted to train a set of images (to detect objects like alcohol glasses/bottles) using CNTK - ResNet/Fast-R CNN.
I am trying to follow below documentation from GitHub; However, it does not appear to be a straight forward procedure. https://github.com/Microsoft/CNTK/wiki/Object-Detection-using-Fast-R-CNN
I cannot find proper documentation to generate ROI's for the images with different sizes and shapes. And how to create object labels based on the trained models? Can someone point out to a proper documentation or training link using which I can work on the cntk model? Please see the attached image in which I was able to load a sample image with default ROI's in the script. How do I properly set the size and label the object in the image ? Thanks in advance!
sample image loaded for training
Not sure what you mean by proper documentation. This is an implementation of the paper (https://arxiv.org/pdf/1504.08083.pdf). Looks like you are trying to generate ROI's. Can you look through the helper functions as documented at the site to parse what you might need:
To run the toy example, make sure that in PARAMETERS.py the datasetName is set to "grocery".
Run A1_GenerateInputROIs.py to generate the input ROIs for training and testing.
Run A2_RunCntk_py3.py to train a Fast R-CNN model using the CNTK Python API and compute test results.
The algo will work on several candidate regions and then generate outputs: one for the classes of objects and another one that generates the bounding boxes for the objects belonging to those classes. Please refer to the code for getting the details of the implementation.
Can someone point out to a proper documentation or training link using which I can work on the cntk model?
You can take a look at my repository on GitHub.
It will guide you through all the steps required to train your own model for object detection and classification with CNTK.
But in short the proper steps should look something like this:
Setup environment
Prepare data
Tag images (ground truth)
Download pretrained model and create mappings for your custom dataset
Run training
Evaluate the model on test set
For the toy example A2 part of the Beta 12 Release, it is said that there are two option for training:
A2_RunCntk_py3.py (python API)
A2_RunCntk.py (brain_script)
Are the models trained from these two methods the same? Or in other words, can I load the model from brain_script into python API and then detect other testing images?
Also see Object Detection using Fast R CNN.
Yes it is possible to use Python to load a model you trained with Brainscript. A few gotchas in doing this correctly are described here. We are working on making things work seamlessly without too much Python code for massaging the data.