# Tag Info

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There are various dataset available such as Pascal VOC dataset: You can perform all your task with these. size of the dataset is as follows ADE20K Semantic Segmentation Dataset: you can perform only segmentation here COCO dataset: This is rich dataset but a size larger then 5 GB so you can try downloading using google colab in your drive and then make ...

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Is the image taken from a constant distance? If yes, you'd need to scale the images to the same dimensions first of all. For few images say 100-500 images (more the better) you'd need to label the dataset by proper scaling. Once labeled, use it to train a CNN (Although best would be training a ResNet). Once trained with decent accuracy, test it for the ...

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Is this a good approach? Will I have a lot of trouble with different backgrounds? A lot will depend on the nature of the backgrounds you have, and how well they encode/decode by themselves without the object in frame. My gut feeling is that your system will have poor performance compared to a properly trained classifier, as the autoencoder will naturally ...

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I guess that they need so little data because their models are already trained on huge datasets, and they are just transferring the learning (using those pre-trained models as starting point).

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If you have stero pairs, and you can identify the objects in the scene, you do not need a neural network, you can just use triangulation. If you need to identify which objects in the scene are the same, you have an image segmentation problem. Depending on your problem and the amount of data you have access to, you may be able to use simple techniques like ...

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You should use both classes together. Let's say you use the method you proposed. Then they will be contradicting each other as one teaches the network to recognize people, not objects and the other teaches the network to recognizes objects not person. There is no need for seperation of the two classes, unless you are making two seperate classifier. Hope I ...

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I'm working on a similar problem. I'm using a 2D point cloud of an object, for example, X and Y coordinates for height, and with that more simple data set I will train a regression model (currently working on that). In my opinion, this approach with dissecting complex point cloud into cross sections that contain wanted dimension and feeding that to the model ...

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Yes you can classify people bounding box with object detection. State of the art object detection model have people as one of the class in the object detection, as shown here: As you can see the image have both object bounding box and people bounding box.

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This could be possible, providing you have the right dataset to train it on. The volume of a cup consist of width, height and depth. You can probably detect all three of those given the bounding box or the pixels of the cup. However detecting the dimensions of an object require a reference object, like a penny or your finger and you have to specify the ...

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After a quick scan, it would seem that, in the history of object detection, machine learning has always been at the forefront. Before then, it would just be a heuristic approach. For a quick answer, here: https://towardsdatascience.com/real-time-object-detection-without-machine-learning-5139b399ee7d That goes over object detection without using machine ...

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Precision is the number of true positives over the number of predicted positives(PP), and recall is the number of true positives(TP) over the number of actual positives(AP) you get. I used the initials just to make it easier ahead. A true positive is when you predict a car in a place and there is a car in that place. A predicted positive is every car you ...

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I think you underestimate the size of YOLO. This is the size of one segment of yolo tiny according to the darknet .cfg file: Convolutional Neural Network structure: 416x416x3 Input image 416x416x16 Convolutional layer: 3x3x16, stride = 1, padding = 1 208x208x16 Max pooling layer: 2x2, stride = 2 208x208x32 ...

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You might want to look into building convolutional neural network (CNN) for object detection using Keras. With plain white squares, it should work pretty good.

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I think there is no absolute answer for this. Often its kind of trial and error. In general the CNN tries to generalize the problem, so using all logos with different augmentations and ground truths can maybe lead to some feature maps, which are so general that the CNN can find logos. But if your logos are so various, and embedded in colorful websites, the ...

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Just by having more parameters, the deeper model has a higher capacity than the smaller one. This means that theoretically it can learn to extract more complex features from the data. Additionally, more layers means that the model can extract even higher-level features from the data. So, generally speaking, deeper models will most of the times outperform ...

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It depends upon the factors such as 1. Batch size (GPU memory capacity) 2. CPU speed and number of cores(multi-threading to load the images) Number of classes increase the number of convolution filters only in the prediction layers of YOLO. It influences only less than 1% speed of the detector to train the model.

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Here are some articles, the first three include code: Deep Learning for Traffic Signs Recognition, by Moataz Elmasry, April 2, 2018 Traffic Sign Classification with Keras and Deep Learning, by Adrian Rosebrock, November 4, 2019 Traffic Sign Detection using Convolutional Neural Network, by Sanket Doshi, September 1, 2019 AI to manage road infrastructure via ...

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Maybe this is helpful: Recognising Traffic Signs With 98% Accuracy Using Deep Learning, by Eddie Forson. Greetings Mario

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In fact, choosing smartly the values of the image augmentation can help the performance of your system. Where I work we developed an object detector for cars. We had the following image augmentation parameters: Apect ratio distorsion: it changed the cars dimensions Additive noise: it blurred the image Change colorspace: change the cars colors Saturation ...

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If you want to evaluate on real thermal image dataset, you can use this one. Thermal Image dataset is mAP a relevant metric when I want to show result to a client ? (e.g a client doesn't understand if I tell him "my model has a mAP=0.7") Mean Average Precision is the relevant metric but it's more technical. You can start explaining with False Positives ...

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NDDS is a UE4 plugin from NVIDIA to empower computer vision researchers to export high -quality synthetic images with metadata. NDDS supports images, segmentation, depth, object pose, bounding box, keypoints, and custom stencils. In addition to the expo rter, the plugin includes different components for generating highly randomized images. This randomization ...

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This looks like it could be a homework problem, so consider updating it with the homework tag if so. The second model has the same precision, but worse recall, than model 1. Therefore we would rather have model 1 than model 2. The third model has worse recall than model 1, and worse precision than model 1, therefore we would rather have model 1 than model ...

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You can use the dataset test set as "frames" of video. Test the images with your model and calculate the images per second of the result and that is the same as frames per second. However you should set the batch size to 1 as in the real world scenario. You should also display each image with teh corresponding boxes after inference and remove the accuracy ...

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I will consider that you need to extract(crop) the digram from the pdf research paper. You can use PyPDF2 or PyMuPDF to extract the images from the PDF file and then you can apply machine learning to do recognition and classification of the images. There are different types of machine learning solutions for image classification and you can start with ...

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Not necessarily. Supposing your data is from the distribution of possible images containing an upright person close to the camera. Something like a neural network would perform poorly on the new data since it comes from a distribution other than on what it was trained. You could try augmenting the dataset to try to get some synthetic "far away upside down ...

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Fisheye camera is always worst. Both convolutional networks object detectors and feature-based object detectors rely on the "isometry" of planar image - lack of strong distortions. Multiple camera have added benefit of several independent sources of information - ensembling. If each camera processed by separate network that may help in verification of ...

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This is called decomposition of multi-class classifier. Your proposed method is called one vs all. One vs. all provides a way to leverage binary classification. Given a classification problem with $N$ possible solutions, a one-vs.-all solution consists of $N$ separate binary classifiers—one binary classifier for each possible outcome. During training, the ...

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Personally, I'd say as long as the object is visible don't do either. If the model has been well built and if lighting changes would help, the convolution operation weights would learn an operation similar to contrast or brightness changes. On the other hand if the object visibility is an issue, then natural lighting changes would be better, due to the lack ...

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