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New to machine learning so looking for some direction how to get started. The end goal is to be able to train a model to count the number of objects in an image using Tensorflow. My initial focus will be to train the model to count one specific type of object. So lets say I take coins. I will only train the model to count coins. Not worried about creating a generic counter for all different types of objects. I've only done Google's example of image classification of flowers and I understand the basics of that. So looking for clues how to get started. Is this an image classification problem and I can use the same logic as the flowers...etc etc?
Probably the best performing solution for the coin problem would be to use a regression to solve this. Annotate 5k images with the amount of objects in the scene and run your model on it. Then your model just outputs the correct number. (Hopefully)
Another way is to classify if an image shows a coin and use a sliding window approach like this one: https://arxiv.org/pdf/1312.6229.pdf to classify for each window if it shows a coin. Then you count the found regions. This one is easier to annotate and learn and better extensible. But you have the problem of choosing good windows and using the result of those windows in a concise way.
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I am working on building a custom facial recognition for our office.
I am planning to use Google FaceNet,
Now my question is that you can find or create your own version of facenet model in keras or pytorch there's no issue in that, but regarding creating dataset ,I want to know what are the best practices to capture photo of person when I don't have any prior photo of that person,all I have is a camera and a person ,should I create variance in by changing lightning condition or orientation or face size ?
A properly trained FaceNet model should already be somewhat invariant to lighting conditions, pose and other features that should not be a part of identifying a face. At least that is what is claimed in a draft of the FaceNet paper. If you only intend to compare feature vectors generated from the network, and intend to recognize a small group of people, your own dataset likely does not have to be particulary large.
Personally I have done something quite similar to what you are trying to achieve for a group of around ~100 people. The dataset consisted of 1 image per person and I used a 1-N-N classifier to classify the generated feature vectors. While I do not remember the exact results, it did work quite well. The pretrained network's architecture was different from FaceNet's but the overall idea was the same though.
The only way to truly answer your question though would be to experiment and see how well things work out in practice.
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I'm new in NLP and text mining and I'm trying to build a documents classifier.
Once the model is trained, we test it on new documents (they, test-data, don't have labels). It is expected that the model is not 100% accurate; so for misclassified documents, we want interact with a user to correct these bad predictions.
I've two ideas:
Retrain the model where: traindata = old_traindata + data corrected by the user.
After each user's rectification, update model parameters.
Does this sound correct? in the second case, which kind of algorithms should I use? How efficiently can we solve this problem?
You can do this but it will be a very intensive task if you plan on retraining the model on the whole data again and again if it is on a daily basis. Instead of retraining the model completely, you should try transfer learning. Save your model and then load it back and train it on the data corrected by the user. The model will be able to correct it mistakes without losing what it has already learned. The problem with transfer learning is that after some time, it will get fine tuned to the new data that you will have to retrain it from scratch. But this is far better then retraining the model every day.
You should have proper metrics in place to check if your models accuracy starts dropping in the old data after several iterations of "transfer learning". If the accuracy drops, just retrain the model on all of the data till date and you will be good to go.
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Hello everyone I'm building a license plate detection model in Tensorflow. I built a function that chooses a license plate at random from a collection of ~5000 plates and puts it in a random place in on a random background and saves the coordinates. At first I thought to generate about 40K images this way and train the network on with the generated data. But wouldn't it be a good idea to just continiously keep generating new data to feed to the network and basically eliminate any chance of it getting overfitted?
This is an excellent way to train it on how to spot the discontinuities around a superimposed yellow / white / blue rectangle, but maybe not such a great way of teaching it to spot a real license plate. If you've got a good way of procedurally generating images then great! but be warned.
It might spot the wrong pattern.
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I have quite a challenging use case for image recognition. I want to detect composition of mixed recycling e.g. Crushed cans,paper,bottles and detect any anomalies such as glass, bags, shoes etc.
Trying images with the google vision api the results are mainly "trash", "recycling" "plastic" etc likely because the api hasn't been trained on mixed and broken material like this?.
For something like this would I have to go for something like tensor flow and build a neural network from my own images? I guess I wouldn't need to use google for this as tensor flow is open source?
Thanks.
So generally, when ever you apply machine learning to a new, real world use case, it is a good idea to get your hands on a representative dataset, in your case it would be images of these trash materials.
Then you can pick an appropriate detection model (VGG, Inception, ResNet), modify the final classification layer to output as many category labels as you require (maybe 'normal' or 'anomaly' in your case, so 2 classes).
Then you load the pre-trained weights for this network, because the learned features generalize (google 'Transfer Learning'), initialize your modified classification layer randomly, and then train the last layer, maybe train the last two layers, or last three layers (depending on what works best, how much data you have, generalization) etc.
So, in short:
1. Pick a pretrained model.
2. Modify it for your problem.
3. Finetune the weights on your own dataset.
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I am having name and description of event and i want to find out about the categories of the event(for example is it entertainment event, politic event or something else).
I was searching on the web and i looked at some natural language processing techniques such as Latent Dirichlet Allocation but i can not see a way to use it in my situation.
Is it a good idea to try to categorize by having predefined keywords for each category, and then to query the text and decide by the amount of keywords from each category?
Can someone give me a clue about my problem ? Many thanks
One approach you could take is to start simple and use a bayesian classifier to analyze/classify your data.
I would approach this problem by taking your dataset and splitting it into a training dataset and a non-training dataset. Then, manually review each event and categorize it as a type of event. Using this training dataset to run your classifier against the remainder of your data.
This may not be ideal for a large amount of event types but it might be a way for you to get started addressing the problem.