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.
Related
I am trying to track (by detection) objects on a video. The problem is that detected objects' label changed over frames of the video. I believe using RNNs (e.g., LSTMs) may help to make labels more stable but I don't have any idea how to use the frozen model of my object detector (MobilenetV2+SSD) as input for an LSTM layer and train the layer.
you can try this https://github.com/tensorflow/models/tree/master/research/lstm_object_detection. It implementation from Tensorflow mobile video object detection implementation proposed in the following paper:
Mobile Video Object Detection with Temporally-Aware Feature Maps (CVPR 2018).
http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Mobile_Video_Object_CVPR_2018_paper.pdf
Help you for guideline.
I trained a model using Tensorflow object detection API for detecting swimming pools using satellite images. I used 'faster_rcnn_inception_v2_coco_2018_01_28' model for training. I generated a frozen inference graph (.pb). I want to evaluate the precision and recall of the model. Can someone tell me how I can do that, preferably without using pycocotools as I was facing some issues with that. Any suggestions are welcome :)
From the Object Detection API you can run "eval.py" from "models/research/object_detection/legacy/".
Your have to define an evaluation metric in your config file (see the supported evaluation protocols)
For example:
eval_config: {metrics_set: "coco_detection_metrics"}
The Pascal VOC e.g. then gives you the mean Average Precsion (mAP)
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.
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.
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.