What is least dataset for Learning using CNN? - deep-learning

What is the least smallest sample that can be learned using CNN for a research? I have 60 datasets of large images (20, 20, 20) for three classes.


If you're a bit creative then you can really use any amount of pictures (assuming you have at least like 30). If you only have a very small amount of pictures, you just need to use transfer learning instead of training on just your pictures. Essentially you import a pre-trained model (ResNet-50 for example) and then you just continue to train on just your pictures, while freezing the weights in most of the early portions of the network.
By doing this, your network will already know how to detect many key building block features and you'll be able to train your network despite your small dataset.
Here's a link to learn more about transfer learning
https://www.kaggle.com/suniliitb96/tutorial-keras-transfer-learning-with-resnet50

Related

Image Classification on heavy occluded and background camouflage

I am doing a project on image classification on classifying various species of bamboos.
The problems on Kaggle are pretty well labeled, singluar and concise pictures.
But the issue with bamboo is they appear in a cluster in most images sometimes with more than 1 species. Also there is a prevalence of heavy occlusion and background camouflage.
Besides there is not much training data available for this problem.
So I have been making my own dataset by collecting the data from the internet and also clicking images from my DSLR.
My first approach was to use a weighted Mask RCNN for instance segmentation and then classifying it using VGGNet and GoogleNet.
My next approach is to test on Attention UNet, YOLO v3 and a new paper BCNet from ICLR 2021.
And then classify on ResNext, GoogleNet and SENet then compare the results.
Any tips or better approach is much appreciated.

Train a reinforcement learning model with a large amount of images

I am tentatively trying to train a deep reinforcement learning model the maze escaping task, and each time it takes one image as the input (e.g., a different "maze").
Suppose I have about 10K different maze images, and the ideal case is that after training N mazes, my model would do a good job to quickly solve the puzzle in the rest 10K - N images.
I am writing to inquire some good idea/empirical evidences on how to select a good N for the training task.
And in general, how should I estimate and enhance the ability of "transfer learning" of my reinforcement model? Make it more generalized?
Any advice or suggestions would be appreciate it very much. Thanks.

Firstly,
I strongly recommend you to use 2D arrays for the maps of the mazes instead of images, it would do your model a huge favor, becuse it's a more feature extracted approach. try using 2D arrays in which walls are demonstrated by ones upon the ground of zeros.
And about finding the optimized N:
Your model architecture is way more important than the share of training data in all of the data or the batch sizes. It's better to make a well designed model and then to find the optimized amount of N by testing different Ns(becuse it is only one variable, the process of optimizing N can be easily done by you yourself).

Retrain or fine-tuning in Caffe a network with images of the existing categories

I'm quite new to caffe and this could be a non sense question.
I have trained my network from scratch. It trains well and gets a reasonable accuracy in tests. The question is about retraining or fine tuning this network. Suppose you have new samples of images of the same original categories and you want to teach the net with this new images (because for example the net fails to predict in this particular images).
As far a I know it is possible to resume training with a snapshot and solverstate or fine-tuning using only the weights of the training model. What is the best option in this case?. or is better to retrain the net with original images and new ones together?.
Think in a possible "incremental training" scheme, because not all the cases for a particular category are available in the initial training. Is it possible to retrain the net only with the new samples?. Should I change the learning rate or maintain any parameters in order to maintain the original accuracy in prediction when training with the new samples? the net should predict in original image set with the same behaviour after fine tuning.
Thanks in advance.

how to set up the appropriate model setting and layers for high intra-class variation

all experts
I am new in CNN and Caffe. I have a task in classification between 2 classes. The data set that I have collected is very small about 50 for class A and 50 for class B (I know that it is very very small). It is a human images.
I took the BVLC model and made a change such as Batch size for testing and training and also the maximum iteration. I try with many various setup, but the model doesn't work.
Any idea about how to come up with appropriate model or setting or other solutions ?
remark** I once randomly made a change to the BVLC model setup and it worked, but i lost the set up file.
For the train.prototxt and Solve.prototxt, I get it from this guy Adil Moujahid
I did try training batch size as 32,64,128,256 and testing for 5,20,30 but failed
For the data set, it is images of normal women and beautiful women and i will classify it, but Stackoverflow does not allowed me to add more than 2 links
I wonder that is there any formula , equation or steps that I can come up with and choose the right model setting.
Thank you in advance.

What is your meaning in "doesn't work"? Loss stays too high? Training is converged, but accuracy is low? Andrew Ng has an excellent session on "debugging" CNNs - Nuts and Bolts of Building Applications using Deep Learning (NIPS slides, summary, additional summary).
My humble guess is that your network has an overfitting problem - it learns the specific examples and can't generalize - so increasing the train dataset / regularization / data augmentation can help.

Training model to recognize one specific object (or scene)

I am trying to train a learning model to recognize one specific scene. For example, say I would like to train it to recognize pictures taken at an amusement park and I already have 10 thousand pictures taken at an amusement park. I would like to train this model with those pictures so that it would be able to give a score for other pictures of the probability that they were taken at an amusement park. How do I do that?
Considering this is an image recognition problem, I would probably use a convolutional neural network, but I am not quite sure how to train it in this case.
Thanks!

There are several possible ways. The most trivial one is to collect a large number of negative examples (images from other places) and train a two-class model.
The second approach would be to train a network to extract meaningful low-dimensional representations from an input image (embeddings). Here you can use siamese training to explicitly train the network to learn similarities between images. Such an approach is employed for face recognition, for instance (see FaceNet). Having such embeddings, you can use some well-established methods for outlier detections, for instance, 1-class SVM, or any other classifier. In this case you also need negative examples.
I would heavily augment your data using image cropping - it is the most obvious way to increase the amount of training data in your case.
In general, your success in this task strongly depends on the task statement (are restricted to parks only, or any kind of place) and the proper data.