This is a bit of an abstract question.
I have a group of 28x28 px images from certain people, and I would like to label that data with each person who wrote it. How would I go about labeling it for training and testing? This is my first neural network, and I'm having difficulty finding any tutorials that suit my particular need. It feels like most Data, like MNIST/EMNIST, are already labeled.
Some more info is that I'm using Python 3, and Keras with Tensorflow backend.
I am assuming that you know who wrote each image. Then this is a matter of associating that information (the class label) with each image. There are several ways of doing this. Two common approaches are:
Folder structure
Make a folder for each class (person), and put the images inside.
Folder contents:
john/01.png
john/02.png
jane/03.png
susan/...
CSV file
In this case the images can be all in one folder, and then a dedicate Comma-Separated-Values file is used to contain
Folder contents:
dataset.csv
images/01.png
images/02.png
images/03.png
images/....
dataset.csv contents:
filename,person
images/01.png,john
images/02.png,john
images/03.png,jane
...
The CSV approach is nice if you have additional data about each file that you want to store. For instance metadata that could be relevant such as who recorded the file, when was it recorded, with what kind of equipment, what locations etc.
Combinations of the two are also possible, of course.
Related
As I am working on my project that is to detect FOD (Foreign Object Debirs) that is found on the runway. FOD include anything like nuts, bolts, screws, locking wires, plastic debris, stones etc. that has the potential to cause damage to the aircraft. Now I have searched on the Internet to find any image dataset but no dataset is available related to FOD. Now my question is kindly guide me that how can I make my own dataset of images that can then be used for training purpose.
Kindly guide me in making image dataset for both classification and detection purposes. And also the data pre-processing that will be required. Thanks and waiting for the reply!
Although the question is a bit vague regarding your requirements and the specs of your machine, I'll try to answer it. You'll need object detection to do your task. There are many models available which you can use like Yolo, SSD, etc..
To create your own dataset, you can follow these steps:
Take lots of images of your objects of interest in various conditions, viewpoints and backgrounds. (Around 2000 per class should be good enough).
Now annotate (or mark) where your object is in the image. If you're using Yolo, make use of Yolo-mark for annotating. There should be other similar tools for SSD and other models.
Now you can begin training.
These steps should get you started or at least point you in the right direction.
You can build your own dataset with this code. I wrote it, and it works correctly.
You need to import the libraries and add your DATADIR.
if __name__ == "__main__":
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array = cv2.imread(os.path.join(path,img))
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
training_data.append([new_array, class_num])
except Exception as e:
pass
for features, label in training_data:
x_train.append(features)
y_train.append(label)
#create pikle
pickle_out = open("x_train.pickle", "wb")
pickle.dump(x_train, pickle_out)
pickle_out.close()
pickle_out = open("y_train.pickle", "wb")
pickle.dump(y_train, pickle_out)
pickle_out.close()
In case if you're starting completely from scratch, you can use "Dataset Directory", available on Play store. The App helps you in creating custom datasets using your mobile. You'll have to sign in to your Google drive such that your dataset is stored in Drive rather on your mobile. Additionally, It also contains Labelling the entity for classification and Regression predictive models.
Currently, the App supports Binary Image Classification and Image Regression.
Hope this Helped!
Download Link :
https://play.google.com/store/apps/details?id=com.applaud.datasetdirectory
Pretty much brand new to ML here. I'm trying to create a hand-detection CoreML model using turicreate.
The dataset I'm using is from https://github.com/aurooj/Hand-Segmentation-in-the-Wild , which provides images of hands from an egocentric perspective, along with masks for the images. I'm following the steps in turicreate's "Data Preparation" (https://github.com/apple/turicreate/blob/master/userguide/object_detection/data-preparation.md) step-by-step to create the SFrame. Checking the contents of the variables throughout this process, there doesn't appear to be anything wrong.
Following data preparation, I follow the steps in the "Introductory Example" section of https://github.com/apple/turicreate/tree/master/userguide/object_detection
I get the hint of an error when turicreate is performing iterations to create the model. There doesn't appear to be any Loss at all, which doesn't seem right.
After the model is created, I try to test it with a test_data portion of the SFrame. The results of these predictions are just empty arrays though, which is obviously not right.
After exporting the model as a CoreML .mlmodel and trying it out in an app, it is unable to recognize anything (not surprisingly).
Me being completely new to model creation, I can't figure out what might be wrong. The dataset seems quite accurate to me. The only changes I made to the dataset were that some of the masks didn't have explicit file extensions (they are PNGs), so I added the .png extension. I also renamed the images to follow turicreate's tutorial formats (i.e. vid4frame025.image.png and vid4frame025.mask.0.png. Again, the SFrame creation process using this data seems correct at each step. I was able to follow the process with turicreate's tutorial dataset (bikes and cars) successfully. Any ideas on what might be going wrong?
I found the problem, and it basically stemmed from my unfamiliarity with Python.
In one part of the Data Preparation section, after creating bounding boxes out of the mask images, each annotation is assigned a 'label' indicating the type of object the annotation is meant to be. My data had a different name format than the tutorial's data, so rather than each annotation having 'label': 'bike', my annotations had 'label': 'vid4frame25`, 'label': 'vid4frame26', etc.
Correcting this such that each annotation has 'label': 'hand' seems to have corrected this (or at least it's creating a legitimate-seeming model so far).
Are the labels used for training and the ones used for validation the same? I thought they should be the same; however, there seem to be a discrepancy in the labels that are available online. When I downloaded the imagenet 2012 labels for its validation data from the official website, I get labels that start with kit_fox as the first label, which matches the exact 2012's dataset validation images I downloaded from the official website. This is the example of the labels: https://gist.github.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57
However, for almost all the pretrained models, including those trained by Google, the imagenet labels they use for training, actually start with tench, tinca tinca instead. See here: https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
Why is there such a huge discrepancy? Where did the 'tinca tinca' kind of labels come from?
If we use the first label mapping that corresponds to the actual validation images, we face another problem: 2 classes ("Crane" and "maillot") are actually duplicated, i.e. they have the same name but refer to different kind of crane - the mechanical crane and the animal crane - resulting in 100 image in 2 of the classes instead of the supposed 50. If we do not use the first mapping, where is a reliable source of the validation images that correspond to the second label mapping?
I have the same problem in my finetuning. You solve your problem change the name of classes tench, tinca tinca to the synset number. You can find here the mapping
I run caffe using an image_data_layer and don't want to create an LMDB or LevelDB for the data, But The compute_image_mean tool only works with LMDB/LevelDB databases.
Is there a simple solution for creating a mean file from a list of files (the same format that image_data_layer is using)?
You may notice that recent models (e.g., googlenet) do not use a mean file the same size as the input image, but rather a 3-vector representing a mean value per image channel. These values are quite "immune" to the specific dataset used (as long as it is large enough and contains "natural images").
So, as long as you are working with natural images you may use the same values as e.g., GoogLenet is using: B=104, G=117, R=123.
The simplest solution is to create a LMDB or LevelDB database of the image set.
The complicated solution is to write a tool similar to compute_image_mean, which takes image inputs and do the transformations and find the mean!
I know the first step is to create two file lists with the corresponding labels, one for the training and one for the test set. Suppose the former is called train.txt and the latter val.txt. The paths in these file lists should be relative. The labels should start at 0 and look similar to this:
relative/path/img1.jpg 0
relative/path/img2.jpg 0
relative/path/img3.jpg 1
relative/path/img4.jpg 1
relative/path/img5.jpg 2
For each of these two sets, we will create a separate LevelDB. Is this formatted as a text file? I thought I would create a directory with several subdirectories for each of my classes. Do I manually have to create a text file?
Please see this tutorial on how to use convert_imageset to build levelDb or lmdb datasets for caffe's training.
As you can see from these instruction it does not matter how you arrange the image files on your disk (same folder/different folders...) as long as you have the correct paths in your 'train.txt'/'val.txt' files relative to '/path/to/jpegs/' argument. But if you want to use convert_imageset tool, you'll have to create a text file listing all the images you want to use.