I am doing custom object detector using YOLOv4 following this tutorial. What I found in this tutorial is that he used only two dataset (training and validation). So, if I want to use another test dataset, how can I use test dataset.
Actually, I am newbie to the deep learning and I cannot find out the tutorial in which they use test dataset.
Please point me out where and how I can use test dataset. Thank you for your guidance.
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.
How can I evaluate my object detection model in a simple and understandable way, I used the TensorFlow's Object Detection API, but I didn’t understand the Tensorboard graphs. Can I evaluate it manually?
Any help? :(
Welcome to StackOverflow!
In short, yes you can. Yet it could be quite time-consuming to achieve your goal.
Here are the steps you might want to follow(assuming you have some basic understanding of Tensorflow graphs and sessions, otherwise please update your question):
Export your model to a frozen graph(*.pb file) via HERE. This step will give you an out-of-the-box model that you could load without any dependencies of Object Detection API.
Write a script to load your model(frozen graph) and perform the evaluation. Some instructions can be found from HERE. Make sure you use tools such as Netron to check the input and output node names of your frozen graph.
Once you could perform the evaluation, you could write metrics on your own dataset, such as mAP, and loop through all images to get the desired evaluation performed.
You could use the confusion matrix to evaluate your model on the test dataset.
After training the model on your dataset, export the inference graph for evaluation.
Find the attached link which helps you step by step towards evaluation.
Best of luck!
confusion_matrix
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.
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.