I am trying to train custom object classifier in Darknet YOLO v2
https://pjreddie.com/darknet/yolo/
I gathered a dataset for images most of them are 6000 x 4000 px and some lower resolutions as well.
Do I need to resize the images before training to be squared ?
I found that the config uses:
[net]
batch=64
subdivisions=8
height=416
width=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
thats why I was wondering how to use it for different sizes of data sets.
You don't have to resize it, because Darknet will do it instead of you!
It means you really don't need to do that and you can use different image sizes during your training. What you posted above is just network configuration. There should be full network definition as well. And the height and the width tell you what's the network resolution. And it also keeps aspect ratio, check e.g this.
You don't need to resize your database images. PJReddie's YOLO architecture does it by itself keeping the aspect ratio safe (no information will miss) according to the resolution in .cfg file.
For Example, if you have image size 1248 x 936, YOLO will resize it to 416 x 312 and then pad the extra space with black bars to fit into 416 x 416 network.
It is very common to resize images before training. 416x416 is slightly larger than common. Most imagenet models resize and square the images to 256x256 for example. So I would expect the same here. Trying to train on 6000x4000 is going to require a farm of GPUs. The standard process is to square the image to the largest dimension (height, or width), padding with 0's on the shorter side, then resizing using standard image resizing tools like PIL.
You do not need to resize the images, you can directly change the values in darknet.cfg file.
When you open darknet.cfg (yolo-darknet.cfg) file, you can all
hyper-parameters and their values.
As showed in your cfg file images dimensions are (416,416)->(weight,height), you can change the values, so that darknet will automatically resize the images before training.
Since the images have high dimensions, you can adjust batch and sub-division values (lower the values 32,16,8 . it has to be multiples of 2), so that darknet will not crash (memory allocation error)
By default the darknet api changes the size of the images in both inference and training, but in theory any input size w, h = 32 x X where X belongs to a natural number should, W is the width, H the height. By default X = 13, so the input size is w, h = (416, 416). I use this rule with yolov3 in opencv, and it works better the bigger X is.
Related
I have a set of training images that contain many small objects (10-20). The image resolution is high (9000x6000).
Is it better to split the image into the specific objects before running yolo training? Or just leave it as is.
Does yolo resize an entire image, or does it ‘extract’ the annotated object first before resizing?
If it is the former, I am concerned that the resolution will be bad. Imagine 20 objects in a 416x416 image.
Does yolo resize an entire image, or does it ‘extract’ the annotated
object first before resizing?
Yes, an entire image will be resized in case of Yolo and it does not extract annotated object before resizing.
Since your input images have very high resolution, what you can do is:
Yolo can handle object sizes of 25 x 25 effectively with network input layer size 608 x 608. So if your object sizes in original input image are greater than 250 x 250 you can train the images as they are (with 608 x 608 network size). In that case even when images are resized to network size, objects will be of size greater than 25x25. This should give you good accuracy.
(6000/600) * 25 = 250
If object sizes in original images are smaller than 200 x 200, split your input image into 8 smaller units/blocks, say blocks/tiles of 2250 x 1500. Train these blocks as individual images. Each bigger image (9000 x 6000) corresponds to 8 training images. Each image might contain zero to many objects. You can operate in sliding window method.
The method you choose for training should be used for inference as well.
For training on objects of all sizes use following models: [Use this if you use original image as it is used for training]
Yolov4-custom
Yolov3-SPP
Yolov3_5l
If all of the objects that you want to detect are of smaller size, then for effective detection use Yolov4 with following changes: [Use this if you split original image into 8 blocks]
Set layers = 23 instead of layers = 54
Set stride=4 instead of stride=2
Set stride=4 instead of stride=2
References:
Refer this relevant GitHub thread
darknet documentation
I am wondering if YOLO (any version, specially the one with accuracy, not speed) can be trained on the text data. What I am trying to do is to find the Region in the text image where any equation is present.
For example, I want to find the 2 of the Gray regions of interest in this image so that I can outline and eventually, crop the equations separately.
I am asking this questions because :
First of all I have not found a place where the YOLO is used for text data.
Secondly, how can we customise for low resolution unlike the (416,416) as all the images are either cropped or horizontal mostly in (W=2H) format.
I have implemented the YOLO-V3 version for text data but using OpenCv which is basically for CPU. I want to train the model from scratch.
Please help. Any of the Keras, Tensorflow or PyTorch would do.
Here is the code I used for implementing in OpenCv.
net = cv2.dnn.readNet(PATH+"yolov3.weights", PATH+"yolov3.cfg") # build the model. NOTE: This will only use CPU
layer_names = net.getLayerNames() # get all the layer names from the network 254 layers in the network
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()] # output layer is the
# 3 output layers in otal
blob = cv2.dnn.blobFromImage(image=img, scalefactor=0.00392, size=(416,416), mean=(0, 0, 0), swapRB=True,)
# output as numpy array of (1,3,416,416). If you need to change the shape, change it in the config file too
# swap BGR to RGB, scale it to a threshold, resize, subtract it from the mean of 0 for all the RGB values
net.setInput(blob)
outs = net.forward(output_layers) # list of 3 elements for each channel
class_ids = [] # id of classes
confidences = [] # to store all the confidence score of objects present in bounding boxes if 0, no object is present
boxes = [] # to store all the boxes
for out in outs: # get all channels one by one
for detection in out: # get detection one by one
scores = detection[5:] # prob of 80 elements if the object(s) is/are inside the box and if yes, with what prob
class_id = np.argmax(scores) # Which class is dominating inside the list
confidence = scores[class_id]
if confidence > 0.1: # consider only those boxes which have a prob of having an object > 0.55
# grid coordinates
center_x = int(detection[0] * width) # centre X of grid
center_y = int(detection[1] * height) # Center Y of grid
w = int(detection[2] * width) # width
h = int(detection[3] * height) # height
# Rectangle coordinates
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h]) # get all the bounding boxes
confidences.append(float(confidence)) # get all the confidence score
class_ids.append(class_id) # get all the clas ids
Being an object detector Yolo can be used for specific text detection only, not for detecting any text that might be present in the image.
For example Yolo can be trained to do text based logo detection like this:
I want to find the 2 of the Gray regions of interest in this image so
that I can outline and eventually, crop the equations separately.
Your problem statement talks about detecting any equation (math formula) that's present in the image so it can't be done using Yolo alone. I think mathpix is similar to your use-case. They will be using OCR (Optical Character Recognition) system trained and fine tuned towards their use-case.
Eventually to do something like mathpix, OCR system customised for your use case is what you need. There won't be any ready ready made solution out there for this. You'll have to build one.
Proposed Methods:
Mathematical Formula Detection in Heterogeneous Document Images
A Simple Equation Region Detector for Printed Document Images in Tesseract
Note: Tesseract as it is can't be used because it is a pre-trained model trained for reading any character. You can refer 2nd paper to train tesseract towards fitting your use case.
To get some idea about OCR, you can read about it here.
EDIT:
So idea is to build your own OCR to detect something that constitutes equation/math formula rather than detecting every character. You need to have data set where equations are marked. Basically you look for region with math symbols(say summation, integration etc.).
Some Tutorials to train your own OCR:
Tesseract training guide
Creating OCR pipeline using CV and DL
Build OCR pipeline
Build Your OCR
Attention OCR
So idea is that you follow these tutorials to get to know how to train
and build your OCR for any use case and then you read research papers
I mentioned above and also some of the basic ideas I gave above to
build OCR towards your use case.
I'm working on a project that trains an ML model to predict the location of Waldo in a Where's Wally? image using AWS Sagemaker with the underlying object detection algorithm being Single Shot Detection, but I am thinking that using an actual puzzle image with dimensions like 2000 x 2000 as training data is not possible and that SSD will auto-resize the image to 300 x 300 which would render Waldo a meaningless blur. Does SSD re-size images automatically, or will it train on the 2000 x 2000 image? Should I crop resize all puzzles to 300 x 300 images containing Waldo, or can I include a mix of actual puzzle images with dimensions 2000+ x 2000+ and the 300 x 300 cropped images?
I'm considering augmenting the data by cropping these larger images at locations that contain Wally so that I can have 300 x 300 images where Wally isn't reduced to a smudge on a page and is actually visible - is this a good idea? I am thinking that SSD does train on the 2000 x 2000 image, but the FPS will reduce by a lot - is this wrong? I feel like if I don't use the 2000 x 2000 image for training, in the prediction stage where I start feeding the model images with large dimensions (actual puzzle images), the model won't be able to predict locations accurately - is this not the case?
SageMaker object detection resizes the image based on the input parameter "image_shape", which you use a size larger than 300 x 300. But 2000 x 2000 might be too large for the algorithm and it will also slow down the training speed. You can try to use a image size somewhere in the middle. Cropping larger images into small patches is a good idea for solving this problem. For the inference, the input image will also be resized to the same size as the training parameter "image_shape". So you may want to crop or resize the large image before you send them to the endpoint.
Can anybody please explain this basic thing to me that how does a 192x28x28 input image gets reduced to a 16x28x28 feature maps using a 1x1 conv mapping. My question is about the understanding of what exactly happens when 192 goes to 16 ??
i know about ((I-2P-F)/S)+1, but what happens in the process of reducing depth.
The 1x1 Convolution compresses the whole 192*28*28 input image (which could be read as 192 feature maps of 28px * 28px pixels images) into a single 1*28*28 image. So far it reduces depth in the "feature map axis" to 1 while preserving the height and width of the original image.
But then... why do you get the 16? In a convolutional layer you can have different kernels. Basically each kernel is an indepentent filter with the same size. In your case it looks like your 1x1 Conv layer has 16 kernels by default, hence you get 16 28*28 images (one per kernel).
im recently using tensorflow api object detection. The default SSD-MobileNet v1 is using 300 x 300 images as input training image, but i gonna edit the image size as width and height in different values. For instance, 320 * 180. Are aspects ratio in .config represent the real ratio of the anchors width/height ratio or they are just for the square images?
You can change the "size" to any different value , the general guidance is preserve the aspect ratio of the original image while the size can be different value.
Aspect ratios represent the real ratio of anchors. You can use it for different input ratios, but you will get the best result if you use input ratio similar to square images.