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.
I'm quite new to transfer learning. I'm planning to do it using YOLO for a CNN supervised regression task. Given an image, predict the number of times it will be viewed. I'm inclined on using YOLO as it is an object detector. Highly viewed photos mostly contain objects(face, animals, text, etc) that are classes that are in the COCO dataset where YOLO was originally trained.
I already tried using pretrained CNN models(VGGNet, MobileNet, etc.) with frozen weights but the results are not good. The option to fine tune the pretrained models are impossible since I don't have the computational resources to train using 100K+ images for x epochs just to create a good model for my problem.
YOLOuses Darknet as a CNN backbone/feature extractor. Therefore, you may want to try a pre-trained Darknet as a feature extractor and replace the classifier with your regressor. Standard YOLO uses Darknet-53 while Tiny YOLO uses Darknet-19.
I have a dataset composed of 10k-15k pictures for supervised object detection which is very different from Imagenet or Coco (pictures are much darker and represent completely different things, industrial related).
The model currently used is a FasterRCNN which extracts features with a Resnet used as a backbone.
Could train the backbone of the model from scratch in one stage and then train the whole network in another stage be beneficial for the task, instead of loading the network pretrained on Coco and then retraining all the layers of the whole network in a single stage?
From my experience, here are some important points:
your train set is not big enough to train the detector from scratch (though depends on network configuration, fasterrcnn+resnet18 can work). Better to use a pre-trained network on the imagenet;
the domain the network was pre-trained on is not really that important. The network, especially the big one, need to learn all those arches, circles, and other primitive figures in order to use the knowledge for detecting more complex objects;
the brightness of your train images can be important but is not something to stop you from using a pre-trained network;
training from scratch requires much more epochs and much more data. The longer the training is the more complex should be your LR control algorithm. At a minimum, it should not be constant and change the LR based on the cumulative loss. and the initial settings depend on multiple factors, such as network size, augmentations, and the number of epochs;
I played a lot with fasterrcnn+resnet (various number of layers) and the other networks. I recommend you to use maskcnn instead of fasterrcnn. Just command it not to use the masks and not to do the segmentation. I don't know why but it gives much better results.
don't spend your time on mobilenet, with your train set size you will not be able to train it with some reasonable AP and AR. Start with maskrcnn+resnet18 backbone.
what is the difference between R-CNN, fast R-CNN, faster R-CNN and YOLO in terms of the following:
(1) Precision on same image set
(2) Given SAME IMAGE SIZE, the run time
(3) Support for android porting
Considering these three criteria which is the best object localization technique?
R-CNN is the daddy-algorithm for all the mentioned algos, it really provided the path for researchers to build more complex and better algorithm on top of it.
R-CNN, or Region-based Convolutional Neural Network
R-CNN consist of 3 simple steps:
Scan the input image for possible objects using an algorithm called Selective Search, generating ~2000 region proposals
Run a convolutional neural net (CNN) on top of each of these region proposals
Take the output of each CNN and feed it into a) an SVM to classify the region and b) a linear regressor to tighten the bounding box of the object, if such an object exists.
Fast R-CNN:
Fast R-CNN was immediately followed R-CNN. Fast R-CNN is faster and better by the virtue of following points:
Performing feature extraction over the image before proposing regions, thus only running one CNN over the entire image instead of 2000 CNN’s over 2000 overlapping regions
Replacing the SVM with a softmax layer, thus extending the neural network for predictions instead of creating a new model
Intuitively it makes a lot of sense to remove 2000 conv layers and instead take once Convolution and make boxes on top of that.
Faster R-CNN:
One of the drawbacks of Fast R-CNN was the slow selective search algorithm and Faster R-CNN introduced something called Region Proposal network(RPN).
Here’s is the working of the RPN:
At the last layer of an initial CNN, a 3x3 sliding window moves across the feature map and maps it to a lower dimension (e.g. 256-d)
For each sliding-window location, it generates multiple possible regions based on k fixed-ratio anchor boxes (default bounding boxes)
Each region proposal consists of:
an “objectness” score for that region and
4 coordinates representing the bounding box of the region
In other words, we look at each location in our last feature map and consider k different boxes centered around it: a tall box, a wide box, a large box, etc. For each of those boxes, we output whether or not we think it contains an object, and what the coordinates for that box are. This is what it looks like at one sliding window location:
The 2k scores represent the softmax probability of each of the k bounding boxes being on “object.” Notice that although the RPN outputs bounding box coordinates, it does not try to classify any potential objects: its sole job is still proposing object regions. If an anchor box has an “objectness” score above a certain threshold, that box’s coordinates get passed forward as a region proposal.
Once we have our region proposals, we feed them straight into what is essentially a Fast R-CNN. We add a pooling layer, some fully-connected layers, and finally a softmax classification layer and bounding box regressor. In a sense, Faster R-CNN = RPN + Fast R-CNN.
YOLO:
YOLO uses a single CNN network for both classification and localising the object using bounding boxes. This is the architecture of YOLO :
In the end you will have a tensor of shape 1470 i.e 7*7*30 and the structure of the CNN output will be:
The 1470 vector output is divided into three parts, giving the probability, confidence and box coordinates. Each of these three parts is also further divided into 49 small regions, corresponding to the predictions at the 49 cells that form the original image.
In postprocessing steps, we take this 1470 vector output from the network to generate the boxes that with a probability higher than a certain threshold.
I hope you get the understanding of these networks, to answer your question on how the performance of these network differs:
On the same dataset: 'You can be sure that the performance of these networks are in the order they are mentioned, with YOLO being the best and R-CNN being the worst'
Given SAME IMAGE SIZE, the run time: Faster R-CNN achieved much better speeds and a state-of-the-art accuracy. It is worth noting that although future models did a lot to increase detection speeds, few models managed to outperform Faster R-CNN by a significant margin. Faster R-CNN may not be the simplest or fastest method for object detection, but it is still one of the best performing. However researchers have used YOLO for video segmentation and by far its the best and fastest when it comes to video segmentation.
Support for android porting: As far as my knowledge goes, Tensorflow has some android APIs to port to android but I am not sure how these network will perform or even will you be able to port it or not. That again is subjected to hardware and data_size. Can you please provide the hardware and the size so that I will be able to answer it clearly.
The youtube video tagged by #A_Piro gives a nice explanation too.
P.S. I borrowed a lot of material from Joyce Xu Medium blog.
If your are interested in these algorithms you should take a look into this lesson which go through the algoritmhs you named : https://www.youtube.com/watch?v=GxZrEKZfW2o.
PS: There is also a Fast YOLO if I remember well haha !
I have been working with YOLO and FRCNN a lot. To me the YOLO has the best accuracy and speed but if you want to do research on image processing, I will suggest FRCNN as many previous works are done with it, and to do research you really want to be consistent.
For Object detection, I am trying SSD+ Mobilenet. It has a balance of accuracy and speed So it can also be ported to android devices easily with good fps.
It has less accuracy compared to faster rcnn but more speed than other algorithms.
It also has good support for android porting.
I am trying to implement an image search engine using AlexNethttps://github.com/akrizhevsky/cuda-convnet2
The idea is to implement an image search engine by training a neural net to classify images and then using the code from the net's last hidden layer as a similarity measure.
I am trying to figure out how to train the CNN on a new set of images to classify them. Does anyone know how to get started with this?
Thanks
You basically have two approaches to your problem:
-Either you have plenty of good training data (>1M) and dozens of GPUs and you retrain the network from scratch using SGD with the classes you have for your queries.
-Either you don't and then you simply truncate a pretrained AlexNet (where exactly you truncate it is for you to choose) and plug it to your images (possibly resized to fit the network (227x227x3 if I am not mistaken)).
Then from your image you get a feature vector (sometimes called a descriptor) and you use those feature vectors to train a linear SVM on your images and your specific task.