Tensorflow object detection API provides a number of pretrained object detection models to choose from. However, I would like to introduce modifications to the architecture of those models.
Particularly, I would like to make Faster RCNN into a more shallow network and use it to train my model. I want to gain in performance despite loss in accuracy. MobileNet is too inaccurate for my application.
Is it possible to achieve this without having to implement everything from scratch ?
Thank you.
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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'm starting to dive into tensor and object detection for a drone my friend and I are building. I keep seeing the word "model" thrown around and I'm sorry but I don't know what I should be picturing when I see the word "model" in terms of tensorflow and object detection.
Usually, in deep learning model is simply architecture of a neural network. It defines type of layers, number of nodes, connections, etc. Tensorflow uses static graph, which describes your model architecture in terms of nodes and operations. As a start you can use Keras API for defining your model.
https://keras.io/
Also read more about TF graph https://www.tensorflow.org/guide/graphs and take a look at tutorials https://www.tensorflow.org/tutorials
I'm a user of Tensorflow Object Detection API. I use it a lot to train models like Faster-RCNN on my images. From what I understand, there is a backbone network (in my experiences, ResNet) used to extract features.
I would like to re-use the weight of this specific network, but when I save my model, it's a Faster RCNN model and, even after hours in the documentation and in the source files, I don't see how to isolate the weight of the backbone network.
Is something somebody else already has realized before ? Or is Tensorflow OD API not the right tool for what I need ?
Thank you for your help or advises !
I have trained the object detection API using ssd_mobilenet_v1_coco_2017_11_17 model to detect a custom object. But after training, the API only detects the custom object and not the objects for which the API is already trained. ssd_mobilenet_v1_coco_2017_11_17 model detect 90 objects.
Is there any way to add more classes to an existing model so that it can detect new objects along with the one it has been trained for?
This question is already asked here and some answer could be found here.
The very last layer of the networks is softmax layer. when the network is trained, the weights of the network is optimized for the exact number of classes on the training set. So, if you need to add a new class and also the classes it was trained, the easiest way is to get the original dataset it was trained on along with your new class images. Then start the training from the pre-trained model weights. The training should converge faster as it has to do relatively little adjustments.
Has anyone implement the FRCNN for TensorFlow version?
I found some related repos as following:
Implement roi pool layer
Implement fast RCNN based on py-faster-rcnn repo
but for 1: assume the roi pooling layer works (I haven't tried), and there are something need to be implemented as following:
ROI data layer e.g. roidb.
Linear Regression e.g. SmoothL1Loss
ROI pool layer post-processing for end-to-end training which should convert the ROI pooling layer's results to feed into CNN for classifier.
For 2: em...., it seems based on py-faster-rcnn which based on Caffe to prepared pre-processing (e.g. roidb) and feed data into Tensorflow to train the model, it seems weird, so I may not tried it.
So what I want to know is that, will Tensorflow support Faster RCNN in the future?. If not, do I have any mis-understand which mentioned above? or has any repo or someone support that?
Tensorflow has just released an official Object Detection API here, that can be used for instance with their various slim models.
This API contains implementation of various Pipelines for Object Detection, including popular Faster RCNN, with their pre-trained models as well.