I am working on image tagging and annotation problem, simply an image may contain multiple objects. I want to train inception-v4 for multi-label classification. My training data will be an image and a vector of length equals the number of classes and has 1 in each index if the object exists in the image. For example, If I have four classes (Person, car, tree, buildings). If an image contains a person and car. Then my vector will be (1, 1, 0, 0).
What changes do I need to make to train inception-v4 for the tagging and annotation problem?
Do I only need to change the input format and change the loss function from softmax to sigmoid_cross_entropy_with_logits in the inception-v4 architecture?
https://github.com/tensorflow/models/blob/master/slim/nets/inception_v4.py
Thank you in advance.
If you'd like to retrain a model to output different labels, check out the image_retraining example: https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/examples/image_retraining/retrain.py
In that example, we retrain the standard inception v3 model to recognize flowers instead of the standard ImageNet categories.
Related
The labels for YOLO is like [class, x , y, width, height] . Since the dataset is very large, is there any shortcut to generate the labels for YOLO, or we have to hardcode them through measurement?
Method 1: Using Pre-trained YOLOv4 models.
YOLOv4 models were pre-trained on COCO dataset. So, if your object(s) can be found in this list, then, you can use the pre-trained weights to pseudo-label your object(s).
To process a list of images data/new_train.txt and save results of detection in Yolo training format for each image as label <image_name>.txt, use: darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt
Method 2: Using Other Pre-trained Models. It's the same concept. Use other pre-trained models to detect your object (as long as they have trained their models on your object), then export/convert the labels to YOLO format.
Method 3: Use hand-crafted feature descriptors. Examples are shape detection, color-based detection, etc.
Method 4: Manual labelling. If everything else fails, do the labelling yourself or hire some data labelling services. Here's a list of tools that you can use if you want to label them yourself.
The goal is classifying dog and non-dog. The train dataset only contains dog images. Neural network will be trained using this train dataset only and then tested using test dataset that contains dog and non-dog images.
I followed the encoder Datacamp tutorial and in my case, the autoencoder classified all test images as dog which is wrong.
Building CNN for one class classification is not possible too. Any idea how to do this?
The correct term for what you're asking about is binary classification.
And you can safely use a CNN for binary classification. The question is just how you model the output layer. You can either use a softmax layer with two output units (argmax(output) is class 1: dog, class 2: not dog) or use the more traditional method of using a single sigmoid unit as the output (output>0.5: dog, <0.5: no dog). As the softmax normalization is a multiclass extension of sigmoid activation, a sigmoid layer makes more sense on a theoretical basis but if you have trouble implementing this, a softmax should work just as well.
Of course, you'll have to adapt your data labels to fit either approach.
Edit: You need both images of dogs and those that include no dogs. The network needs to know the distributions for both. Just think about your use case and what kind of images you expect that are not dogs. Then collect images that are not dogs and label them accordingly.
I am training a cnn model with five classes using keras library. Using model.predict function i get prediction percentage of the classes. My problem is for a image which doesn't belong to these classes and completely irrelevant, the predict class still predicts the percentages according to the classes.
How do I prevent it? How do I identify it as irrelevant?
I assume you are using a softmax activation on your last layer to generate the probabilities for each class. By definition, the sum of the outputs from the softmax activation must add up to 1. Therefore, it is impossible for the neural net to say that the image does not belong to any of your classes, with your current setup.
There are two potential ways you could address this:
Add another class that represents "other" or "unknown" objects (so you have 6 classes).
Add another output to your neural net (or train a completely independent neural net) that does binary classification on whether or not the image is in one of the 5 classes. That way, if your secondary output says that the image is not in the 5 classes, you can ignore the softmax output.
In both cases, you will need to augment your dataset with images that do not fall in your 5 classes.
Tensorflow is fairly new to me and the way i would have the loss calculated on the mnist dataset was using the softmax_cross_entropy_with_logits function.
This function worked on that dataset due to the label input being a single label on each image
What im trying to do is to train a CNN on the mscoco dataset which has multiple labels on the same image with 80 classes total.
Is there a function that makes that possible?
My label input is currently somewhat a modified onehot representation, meaning that for each image i have a list of 80 elements having 0 for categories not in the image and 1 for categories present in an image
I.e. an image with a human and a dog would have a list of [0,1,0,0,1] assuming i have 5 classes with dogs and humans being in index 1 and 4
For multi-label classification problem, you can use the sigmoid function available in tensorflow (tf.nn.sigmoid_cross_entropy_with_logits). It would take the onehot encoded label input along with the final logits layer as its input.
I have tried Denny Britz's code on TensorFlow CNN Text Classification. Currently, the code is fixated on the number of text classifications as provided by the existing model of TensorFlow. For example, the model has 8 types of classifications. But I wish to increase/decrease the number of classifications. Is there a way to make it work?