Let's say we have n images of cat and dog separately and we trained an image classification model to classify a new image with a probability score saying whether it's a cat or a dog.
Now, we are getting images that contains multiple cats and dogs in same image, how can we detect and localize objects(cats and dogs here)?
If it is possible, can we also depict the focus areas considered by model for prediction so that a bounding box could be drawn?
I think you have trouble understanding how a basic object detection works.
I will recommend you to read this paper first:
https://arxiv.org/pdf/1807.05511.pdf
It is possible. You can use Yolo like this example. There is a keras classification based solution.
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I would like to solve a text similarity problem using fasttext. I am able to create model to get text classification labels using fasttext. But I would like to get document vector which is created for classification layer input generating fasttext model. Then using some similarity methods get the scores .
How can I do that ? Any help would be great
Thanks
I'm fairly sure FastText's supervised mode interim value is just an average of all the input text's word-vectors. So you can request the individual word-vectors, then average them together yourself.
I want to train a facial recognition CNN from scratch. I can write a Keras Sequential() model following popular architectures and copying their networks.
I wish to use the LFW dataset, however I am confused regarding the technical methodology. Do I have to crop each face to a tight-fitting box? That seems impractical, as the dataset has 13000+ faces.
Lastly, I know it's stupid, but all I have to do is preprocess the images (of course), then fit the model to these images? What's the exact procedure?
Your question is very open ended. Before preprocessing and fitting the model, you need to understand Object Detection. Once you understand what object detection you will get answer to your 1st question whether you are required to manually crop every 13000 image. The answer is no. However, you will have to draw bounding boxes around faces and assign label to images if they are not available in the training data.
Your second question is very vague . What do you mean by exact procedure? Is it the steps you need to do or how to do preprocessing and fitting of the model in python/or any other language? There are lots of references available on the internet about how to do preprocessing and model training for every specific problem. There are no universal steps which can be applied to any problem
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?
I have been thinking about building a YOLO model for detecting parking lot occupancy, I have all the small segmented out images for every parking space. Can I train YOLO on these small images already divided into separate empty and occupied classes and test it on a test image like the ariel view of a parking lot with say 28 parking spots and the model should detect the occupied and empty spaces.
If yes then can someone guide me how to approach the problem? I will be using YOLO implemented on Keras.
YOLO is a n object detection model. During training, it takes coordinates of bounding boxes in an image as input and learns to identify the images inside such bounding boxes. As per your problem statement, if you have a aerial view of parking lot then draw the bounding boxes, generate xml files (as per your training requirement) and start training. This ideally should give you the desired model to predict.
Free tool to label images - https://github.com/tzutalin/labelImg
Github project to get an idea of how to train Yolo in Keras on custom dataset - https://github.com/experiencor/keras-yolo2
By any means, this is not a perfect tailor made solution for your problem given you haven't provided any code or images. But this is a good place to start.
what approach should i take when I want my CNN multi-class network to output something like [0.1, 0,1] when image doesn't belong
to any class. Using softmax and categorical_crossentropy for multi-class would give me output that sums up to 1 so still not what I want.
I'm new to neural networks so sorry for silly question and thanks in advance for any help.
I think you are gonna think about Bayesian Learning. First, talking about uncertainty.
For example, given several pictures of dog breeds as training data—when a user uploads a photo of his dog—the hypothetical website should return a prediction with rather high confidence. But what should happen if a user uploads a photo of a cat and asks the website to decide on a dog breed?
The above is an example of out of distribution test data. The model has been trained on photos of dogs of different breeds, and has (hopefully) learnt to distinguish between them well. But the model has never seen a cat before, and a photo of a cat would lie outside of the data distribution the model was trained on. This illustrative example can be extended to more serious settings, such as MRI scans with structures a diagnostics system has never observed before, or scenes an autonomous car steering system has never been trained on.
A possible desired behaviour of a model in such cases would be to return a prediction (attempting to extrapolate far away from our observed data), but return an answer with the added information that the point lies outside of the data distribution. We want our model to possess some quantity conveying a high level of uncertainty with such inputs (alternatively, conveying low confidence).
Then, I think you could read briefly this paper when they also apply to classification task and generate uncertainty for classes (dog, cat...). From this paper, you can extend your finding to application using this paper, and I think you will find what you want.