Let start by saying that i have 2 pre-trained models (in hdf5 files):
The first model is a YOLO-based model, trained on dataset A, which is used to locate human in any images (note that: a trained images o this model may contain many people inside)
The second model is a CNN model which is used to detect gender of a person (male or female) based on the image which only contains 1 person.
Suppose that i only want to use these 2 models and do not want to re-train or modify anything on the dataset. How could i locate female person in a picture of Dataset A?
A possible solution that i think could work:
First use the first model to detect, that is to create bounding boxes around persons in the images.
Crop the bounding boxes into unique images. Feed those images to the second model to see if that person is Female/Male
However, this solution is slow in performance. So is there anyway that can festen this solution or perform this task in different ways?
Related
I have a dataset that is already labeled with specific class names and it is saved on my Computer as:
Train Dataset :
-5_1
-5_2
-5_3
etc...
Where the subfolders(5_1, 5_2, etc.) are the classes of the images. I want to use semi-supervised training where both labeled and unlabeled images must be used. But I don’t know how to “erase” classes from my Dataset in order make them unlabeled and load them to my CNN.
For the labeled images I use datasets.ImageFolder() and DataLoader() so I can load them for training.
Thanks for the help!
PS1: I thought to save them in a different folder named as “Unlabeled” but I am sure that this is gonna use the name of the folder as a new class and this is something that it’s gonna ruin the predictions in training as well in testing
PS2: I must inform you that in this specific time I can't use any other pretrained dataset as CIFAR or MNIST where they already have unlabeled data.
I tried to create my own dataset as a new class but I am confused to the point where I must delete the classes.
I am doing a image classification project using CNN in keras. I have a dataset of about 900 photos of about 70 people .Each person has multiple photos of his different age.
My goal is to predict the correct ID of the person if any one of his photo is in the input.
Here is the glimpse of the data.
My questions are:
What should be my target column ?Is Target 'AGE' or 'ID'? 2-Do I
need to do hot-encoding of the target column? For example if I used
ID as my target,then do I have to do one-hot-encoding of ID column?
If I used ID as my target,then after one-hot-encoding, does it
mean,I will be having 70 classes?
I need information about the
output layer. My goal is to find whether the photo belong to the
same ID or not,so what should be the output layer? Shall I use
softmax with 70 outputs ?
Another question about the output layer
is that can I use a softmax with 70 outputs and then feed it to a
layer of sigmoid with single output ?
You are going to identify the same person using different age images. For example, in the dataset, you have 100 different images of khan and you trained a model. Now you provide the 101st image of khan, the model will detect it. So your target column should be ID.
yes, there are 70 classes and you get one hot encoded vector of 900x70
It should be a softmax layer because the sigmoid layer is used for binary class or multilabel problem. As you have to detect 70 different people from each other, you need a softmax class.
I don't think so, in this way your model would not be capable of telling which person image is this (the one provided as a test)
I have trained two models on different datasets and saved weights of each model as ModelA.h5 and ModelB.h5
I want to average these weights and create a new folder called ModelC.h5 and load it on the same model architechture.
How do I do it?
Model trained on different datasets can't just be added like this. It looks something like this. Let's say like this, train one person to classify 1000 images into 5 classes, then, train another person to classify another 1000 images into same 5 classes. Now, you want to combine them into one.
Rather, what you can do is take ensemble of both the networks. There are multiple ways to ensemble the predictions of both models using Max Voting, Averaging or Weighted Average, Bagging and Boosting, etc. Ensemble helps to boost the weak classifiers into one strong classifier.
You can refer to this link to read more about different types of ensemble: Link
The aim of my project is extracting specific facial features on mobile phone. This a verification application using user's face. Given two different images of the same person, extracting the features as close as possible.
Right now, I use the pretrained model and weights of VGGFace team as a feature extractor, you can download the model in here. However, when I extracted features based on the model, the result was not good enough, I described what I did and what I want as below:
I extract features from Emma Watson' images, image_1 returns feature_1, image2 returns feature_2 and so on (vector length = 2048). If feature[i] > 0.0, convert it to 1.
for i in range(0, 2048):
if feature1[0][i] > 0.0:
feature1[0][i] = 1
Then, I compare the two features vector using Hamming distance. Hamming distance is just a naive way to compare, in real project, I will quantize those features before comparing. However, the distance between 2 images of Emma still large even though I use 2 neural facial expression images (same emotion, different emotion type return worse result).
My question is how could I train the model to extract features of target user. Imaging, Emma is a target user, and her phone only need to extract her features. When someone try to unlock Emma's phone, her phone extract this person's face then compare with saved Emma's features. In addition, I don't want to train a model to classify 2 classes Emma and not Emma. The thing I need is comparing extracted features.
To sum up, If we compare features from different images of the same person, the distance (differences) should be "close" (small). If we compare features from different images of different people, the distance should be "far" (large).
Thank you so much.
I'd do the following: We want to compute the features from a deep layer from a ConvNet to ultimately compare new images with a base image. Let's say this deep layer gives you the feature vector f. Now, create a dataset with pairs of images and a label y. Say, y = 1 if both images are of same person as the base image and y = 0 if they are different. Then, calculate the element wise difference and feed it into a logistic regression unit to get your y_hat: y_hat = sigmoid(np.multiply(W, np.sum(abs(f1 - f2)) + b). You will have to create a "Siamese" network where you have two same ConvNets, one giving you f1 for one image and another one for f2 for another image from the same example pair. Siamese networks need to have the exact weights at all times so you will need to ensure that their weights are same as each other at all times. As you train this new network, you should get desired results.
I am trying to build a CNN classifier using keras to classify if the image is monkey or not. So I have two categories. One with monkey pictures. The other category is a random collection of pictures which is basically any picture that doesn't contain a monkey in it. Is appropriate to have a dataset with one class that is random and the other class with a specific label (rather than having 2 specific classes like cats and dogs)? will a CNN work for this kind of problem?
Thanks!
yes .. CNN will work either you have one label set or many . If you need more details refer this link. This will save your day ..
https://www.pyimagesearch.com/2017/12/11/image-classification-with-keras-and-deep-learning/