6 votes

How does the bounding box regressor work in Fast R-CNN?

A very clear and in-depth explanation is provided by the slow R-CNN paper by Author(Girshick et. al) on page 12: C. Bounding-box regression and I simply paste here for quick reading: Moreover, the ...
Anu's user avatar
  • 328
4 votes

How does YOLO algorithm detect objects if the grid size is way smaller than the object in the test image?

Each grid predictor in YOLO should only have a high score that an object is within it, if it detects the centre of the bounding rectangle is inside itself. So a grid point that contains only the wing ...
Neil Slater's user avatar
  • 28.9k
4 votes

How does the bounding box regressor work in Fast R-CNN?

The paper cited does not mention linear regression at all. What it does is using a neural network to predict continuous variables, and refers to that as regression. The regression that is defined (...
David Masip's user avatar
  • 6,051
4 votes

what is darknet and why is it needed for YOLO object detection?

https://pjreddie.com/darknet/ is their website... I cite : "Darknet: Open Source Neural Networks in C Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to ...
Lucas Morin's user avatar
  • 2,196
4 votes
Accepted

What is the origin of YOLO/darknet coordinates

(0, 0) is top left. Here an a helpful blog that goes through all the features in the output vector. This is common in image processing. There are a few reasons ...
n1k31t4's user avatar
  • 14.9k
4 votes

What are advantages or disadvantages of training deep learning model from scratch?

(Suggestions and edits will be appreciated) let us discuss advantages of training a deep learning model from scratch: Building and training NN from scratch is of a great use in the research field. ...
Mukul Verma's user avatar
3 votes

What are advantages or disadvantages of training deep learning model from scratch?

The advantages of training a deep learning model from scratch and of transfer learning are subjective. It depends a lot on the problem you are trying to solve, the time constraints, the availability ...
Nikhil Shah's user avatar
3 votes
Accepted

How to label overlapping objects for deep learning model training

There is no common practice in labeling the bounding boxes. It is always problem dependent. For example, if you want to count the chickens then you should also label the whole chicken as one instance ...
MachineLearner's user avatar
3 votes

YOLO algorithm - understanding training data

For each bounding box you need p_c: any object / no object (background) b_x, b_y, ...
oezguensi's user avatar
  • 602
3 votes
Accepted

YOLO pretraining

1) The goal of using Average pooling layer (at least here), is to have a vector after it. That way you have a fully connected layer vector. In Yolo, the layer previous the fully connected one seems ...
Jérémy Blain's user avatar
3 votes

Should there be a flat layer in between the conv layers and dense layer in YOLO?

Not sure that still matters for your project but it is important: the Dense layer does not flatten the entry first! It takes the last dimension of the entry tensor and connects it to the neurons of ...
Jérémy Nadal's user avatar
3 votes

Which is the "BEST" deep learning model for "Custom" object detection for images & real time. YOLO v3, v4, v5, EfficientDet?

There are many subtle differences between yolov4 and v5 other than speed like YOLOv4 exceeds YOLOv5's performance on the COCO benchmark.this link will help you explain. YOLOv4 VS YOLOv5. I dont know ...
Shiv's user avatar
  • 679
3 votes

Car Make and Model detection

For detecting the make and model of cars from images with high accuracy across a large number of classes, I would recommend a convolutional neural network (CNN) architecture tailored for fine-grained ...
Multivac's user avatar
  • 2,959
2 votes

Should there be a flat layer in between the conv layers and dense layer in YOLO?

It doesn't matter, with or without flattening, a Dense layer takes the whole previous layer as input. The spatial structure information is not used anymore. Some Neural Network implementations might ...
Jan van der Vegt's user avatar
2 votes

Why does Pascal VOC 2007 dataset have almost 2500 images for 'train' and same number for 'val'? Val should have less images

The size of your validation matters only for the precision of your validation score. Every sample in your validation has some score for validation. The main goal is to be able to say how well your ...
Jan van der Vegt's user avatar
2 votes
Accepted

Last layers of YOLO

You can use the Flatten and Reshape layers to go to Dense and back to HWC format. The last layers in keras would look like this: ...
kenny's user avatar
  • 435
2 votes

what is darknet and why is it needed for YOLO object detection?

Darknet is mainly for Object Detection, and have different architecture, features than other deep learning frameworks. It is faster than many other NN architectures and approaches like FasterRCNN etc. ...
Awais Bajwa's user avatar
2 votes

YOLO annotation guildelines: overlapping and partially visible objects

There is only 1 restriction, your truth bboxes with the same class_id shouldn't be overlapped more than IoU > 45%, because Yolo uses nms_threshold = 0.45. In general, you should mark your objects in ...
Alex's user avatar
  • 649
2 votes

Does YOLO give preference to color over shape or vice-versa while detecting an object?

YOLO has 3 input channels (RGB typically) and is a CNN, a neural net that uses stacked (trainable) convolution filters. Single convolution filters act as feature detectors and can be (trained to be) ...
S van Balen's user avatar
  • 1,364
2 votes

how do the number of classes in an object detection model affect accuracy?

The general consensus in machine learning problems is that it becomes tougher to get higher accuracy results when there is more data with more class splits. The simplest of examples would be cifar 10 ...
Academic's user avatar
  • 482
2 votes
Accepted

Yolov3 Tiny: What do each of the 2535 cells detect?

As the blog mentioned, each cell predicts three things - bbox coords (tx,ty,tw,th) objectness score (po) class scores (p0 - pc) and again each cell predicts three boxes. Hence you get that big red ...
Devashish Prasad's user avatar
2 votes
Accepted

Training a YOLO-style object detector

As rightly said by @Nikos M., it is based on trial and error. And here are some tips you might find useful - Create a good enough validation set. Use YOLO-tiny versions instead of custom architecture....
Devashish Prasad's user avatar
2 votes

Computing F1 score for YOLOV5

The F-measure is the weighted harmonic mean of precision (P) and recall (R) of a classifier, taking α=1 (F1 score). It means that both metrics have the same importance. In your graph, the confidence ...
Illustrati's user avatar
2 votes

Algorithms to do a CTRL+F (find object) on an image

Invariant object recognition(IOR), refers to rapid and accurate recognition of objects in the presence of variations such as size, rotation and position. SIFT and SURF are the most popular among them, ...
Archana David's user avatar
2 votes

Should I remove objects labeled "unknown" from my test set?

It depends how you define the task, i.e. what is the goal of the model: In standard classification, an "unknown" category doesn't make sense because there is no homogeneous "unknown&...
Erwan's user avatar
  • 25.3k
2 votes
Accepted

How to interpret annotation data?

The format description can be found on file DATASET.md at the github repo you linked: The bounding box (bbox) format is [top left X position, top left Y position, top left Z position, deltaX, deltaY, ...
noe's user avatar
  • 26.5k
1 vote

YOLO Dense Prediction

It is kind of unfortunate that no further explanations were given in the paper about this. In my opinion Hard negative mining is actively used by architectures like SSD, actually by boosting its ...
tricostume's user avatar
1 vote

YOLO: How many bounding boxes?

From 98 boxes to 3 boxes, it involve many other things as well. x*y*2 = 98, where 2 are the anchor boxes i.e. each grid will predict two bounding box. Non Max Suppression: As correctly said ...
vipin bansal's user avatar
  • 1,262
1 vote

How YOLO training and prediction works for an object fall in multiple grid?

It seems after referring many documents, I found the answer of my question. First, likely to correct my understanding. I thought for labeling, bounding box size (width, height) will always be ...
vipin bansal's user avatar
  • 1,262
1 vote

what is darknet and why is it needed for YOLO object detection?

This deep learning framework is written itself in C but once you train the network you do not need Darknet itself for the inference. OpenCV has built in support for Darknet formats so both model and ...
Audrius Meskauskas's user avatar

Only top scored, non community-wiki answers of a minimum length are eligible