Can some refer me to a good image dataset with multiple classes and has images for training and testing data at the same place ?
Thanks
It would be good to know, what's your intention. Here are some recommendations.
ALOI: http://staff.science.uva.nl/~aloi/
Microsoft Research Cambridge Object Recognition Image Database: http://research.microsoft.com/en-us/downloads/b94de342-60dc-45d0-830b-9f6eff91b301/default.aspx
The PASCAL Visual Object Classes Homepage: http://pascallin.ecs.soton.ac.uk/challenges/VOC/
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I have been recently introduced to the applications of ML in Cybersecurity, and I was interested in working on an application of GANs to generate data for sparse datasets
(Something like this https://becominghuman.ai/deep-learning-can-tackle-the-problem-of-sparse-data-by-augmenting-available-data-e9a4e0f1fc92)
However, I am not aware of the sort of datasets that can be used for this purpose. Could someone guide me through a few example datasets I can use to train a GAN on and to generate data? Are text datasets any good for GAN related generation?
My objective here is to simply understand how this whole process should work. Any help would be appreciated.
Have you come across this repository? I guess it contains datasets and more!
I decided to take a dip into ML and with a lot of trial and error was able to create a model using TS' inception.
To take this a step further, I want to use their Object Detection API. But their input preparation instructions, references the use of Pascal VOC 2012 dataset but I want to do the training on my own dataset.
Does this mean I need to setup my datasets to either Pascal VOC or Oxford IIT format? If yes, how do I go about doing this?
If no (my instinct says this is the case), what are the alternatives of using TS object detection with my own datasets?
Side Note: I know that my trained inception model can't be used for localization because its a classifier
Edit:
For those still looking to achieve this, here is how I went about doing it.
The training jobs in the Tensorflow Object Detection API expect to get TF Record files with certain fields populated with groundtruth data.
You can either set up your data in the same format as the Pascal VOC or Oxford-IIIT examples, or you can just directly create the TFRecord files ignoring the XML formats.
In the latter case, the create_pet_tf_record.py or create_pascal_tf_record.py scripts are likely to still be useful as a reference for which fields the API expects to see and what format they should take. Currently we do not provide a tool that creates these TFRecord files generally, so you will have to write your own.
Except TF Object Detection API you may look at OpenCV Haar Cascades. I was starting my object detection way from that point and if provide well prepared data set it works pretty fine.
There are also many articles and tutorials about creating your own cascades, so it`s easy to start.
I was using this blog, it helps me a lot.
I'm learning Machine Learning with Tensorflow. I've work with some dataset like Iris flower data and Boston House, but all those data's values was float.
Yes I'm looking for a dataset that contain data's values are in string format to practice. Can you give me some suggestions?
Thanks
I provide you just two easy-to-start places:
Tensorflow website has three very good tutorials to deal with word embedding, language modeling and sequence-to-sequence models. I don't have enough reputation to link them directly but you can easily find them here. They provide you with some tensorflow code to deal with human language
Moreover, if you want to build a model from scratch and you need only the dataset, try ntlk corpora. They are easy to download directly from the code.
Facebook's ParlAI project lists a good amount of datasets for Natural Language Processing tasks
IMDB's reviews dataset is also a classic example, also Amazon's reviews for sentiment analysis. If you take a look at kernels posted on Kaggle you'll get a lot of insights about the dataset and the task.
I'm working on a project for which I'd like to create a dataset of drawn faces (similar in concept to the CUFS dataset). Hand-drawing the faces aside, how would I go from "I have uploaded these image files to my computer and have ensured that they all have identical dimensions" to having a ready-to-use dataset? (I'd like to train/test LeNet with this dataset.) I've never created my own dataset before so am pretty unsure as to how to start.
Thanks!
You should convert the images into levelDB or LMDB. You can follow the exsample of convert_mnist_data.cpp.
I am currently using OpenCV3.0 with the hope i will be able to create a program that does 3 things. First, finds faces within a live video feed. Secondly, extracts the locations of facial landmarks using ASM or AAM. Finally, uses a SVM to classify the facial expression on the persons face in the video.
I have done a fair amount of research into this but can't find anywhere the most suitable open source AAM or ASM library to complete this function. Also if possible I would like to be able to train the AAM or ASM to extract the specific face landmarks i require. For example, all the numbered points in the picture linked below:
www.imgur.com/XnbCZXf
If there are any alternatives to what i have suggested to get the required functionality then feel free to suggest them to me.
Thanks in advance for any answers, all advice is welcome to help me along with this project.
In the comments, I see that you are opting to train your own face landmark detector using the dlib library. You had a few questions regarding what training set dlib used to generate their provided "shape_predictor_68_face_landmarks.dat" model.
Some pointers:
The author (Davis King) stated that he used the annotated images from the iBUG 300-W dataset. This dataset has a total of 11,167 images annotated with the 68-point convention. As a standard trick, he also mirrors each image to effectively double the training set size, ie 11,167*2=22334 images. Here's a link to the dataset: http://ibug.doc.ic.ac.uk/resources/facial-point-annotations/
Note: the iBUG 300-W dataset includes two datasets that are not freely/publicly available: XM2VTS, and FRGCv2. Unfortunately, these images make up a majority of the ibug 300-W (7310 images, or 65.5%).
The original paper only trained on the HELEN, AFW, and LFPW datasets. So, you ought to be able to generate a reasonably-good model on only the publicly-available images (HELEN,LFPW,AFW,IBUG), ie 3857 images.
If you Google "one millisecond face alignment kazemi", the paper (and project page) will be the top hits.
You can read more about the details of the training procedure by reading the comments section of this dlib blog post. In particular, he briefly discusses the parameters he chose for training: http://blog.dlib.net/2014/08/real-time-face-pose-estimation.html
With the size of the training set in mind (thousands of images), I don't think you will get acceptable results with just a handful of images. Fortunately, there are many publicly available face datasets out there, including the dataset linked above :)
Hope that helps!
AAM and ASM are pretty old school and results are a little bit disappointing.
Most of Facial landmarks trackers use cascade of patches or deep-learning. You have DLib that performs pretty well (+BSD licence) with this demo, some other on github or a bunch of API as this one that is free to use.
You can also give a look at my project using C++/OpenCV/DLib with all functionalities you quoted and perfectly operational.
Try Stasm4.0.0. It gives approximately 77 points on face.
I advise you to use FaceTracker library. It is written in C++ using OpenCV 2.x. You won't be disappointed on it.