TrashBot | Self Driving Trash Can : https : //youtu.be/7fdM2hHW8yA | Machine Learning library
kandi X-RAY | TrashBot Summary
kandi X-RAY | TrashBot Summary
cd into the ml directory before copy and pasting these commands cd ml. first download your images from google using google_images_download python3 utils/google_images_download.py --keywords "republic services truck" --limit 100 --output_directory data/images --chromedriver loc/to/chromedriver. after you download the images let's move the images from the download folder to data/images for now. let's convert all of our images to jpg before we continue. python3 utils/imgconvert.py --folder data/images. we'll need to annotate our images. set your bounding boxes for your images. we'll use labelimg after compiling labelimg from the link above, you can open it with: python3 path/to/labelimg.py data/images. after we're done labeling our images you'll have a bunch of xml files. we need to convert the xml files that labelimg created to csv. but before we do that let's split up our images into two different folders: train and test. mkdir data/train && mkdir data/test. also create a open-images-model-labels.txt file inside data/ as well and input all of your class names. 1 per line: touch open-images-model-labels.txt. as well as a labels.txt file: labels.txt. a good rule
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Top functions reviewed by kandi - BETA
- Create a squeezenet
- Create a squeezeze tensor model
- Create a Seperable Conv2d Conv2D Conv2d
- Compute the prediction of x
- Compute the classification header
- Download files
- Executor for download executor
- Predict class labels
- End the timer
- Create a convolutional convolution layer
- Create convolutional layer
- Return the image with the given index
- Prune linear layers
- Balance image data
- Returns the number of conv2d filters
- Creates a predictor for a VGG network
- Parse user input
- Compute NMS score
- Return image at given index
- Convert xml file to csv
- Compute the predicted confidence interval
- Creates a MobileNetv1 model
- Download a single image
- Creates a MobileNetV2
- Assign the priors to the target boxes
- Prune the convolutional layers
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I currently have a pipeline in Azure Dev Ops configured to build, copy, archive, and then deploy a Vue.js app to an Azure App Service running on Linux. When I SSH into the server I do see the outputs of the build appearing successfully in the wwwroot folder but when I pull up the URL of the App Service I'm just shown the default landing page. Below are screenshots of my wwwroot folder, the page I'm seeing when I pull up the URL and a copy of the yml file for the pipeline. Is there something else missing from the yml file that I need to add to tell the app service to start the vue.js app by chance, I'm fairly new to this stuff.
...ANSWER
Answered 2021-Apr-21 at 01:06You need to add startup command
on portal.
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