You will get code or an application to segment anything on an image using CLIP prompts
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Project details
The developed code and application allow users to take advantage of the recent advancements in semantic segmentation, i.e. the "segment anything" algorithm. This project includes both code and a streamlit application that performs the following:
• segment anything by clicking on one or more points on an image
• segment anything by giving a prompt as input (CLIP & segment anything algorithms)
• segment anything by using a quantized model (on fewer model parameters)
Apart from this application, I can also guide users to
• finetune the segment anything algorithm on a new dataset
• to automate background tasks, for instance using a Python file to annotate multiple images at once based on a pre-specified parameter setting
The price of this project is negotiable depending on the requirements.
Feel free to reach out for further discussion, before the acceptance of this project.
• segment anything by clicking on one or more points on an image
• segment anything by giving a prompt as input (CLIP & segment anything algorithms)
• segment anything by using a quantized model (on fewer model parameters)
Apart from this application, I can also guide users to
• finetune the segment anything algorithm on a new dataset
• to automate background tasks, for instance using a Python file to annotate multiple images at once based on a pre-specified parameter setting
The price of this project is negotiable depending on the requirements.
Feel free to reach out for further discussion, before the acceptance of this project.
AI Development Type
Deep LearningAI Tools
Keras, PyTorchAI Development Language
PythonWhat's included $450
These options are included with the project scope.
$450
- Delivery Time 2 days
- AI Model Integration
- Model Documentation
- Source Code
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About Lampros
R, Python, Remote Sensing, Deep Learning and Machine Learning Analyst
100%
Job Success
Paramythia, Greece - 6:52 pm local time
I work with R and Rstudio on a daily basis. I can work with machine learning algorithms based on almost all CRAN, Github or Gitlab repositories. I can utilize visualization R packages such as ggplot2, plotly, tmap, leaflet, mapview. I can create shiny applications (shiny.rstudio.com/gallery/) and report my results in .pdf, word, .html or any other available format using Rmarkdown.
Moreover, I'm capable of using the hybrid 'Rcpp' and 'RcppArmadillo' R packages to improve the efficiency of R code and the Keras and Pytorch deep learning libraries for regression, classification, object detection or image segmentation (with or without pre-trained models).
Steps for completing your project
After purchasing the project, send requirements so Lampros can start the project.
Delivery time starts when Lampros receives requirements from you.
Lampros works on your project following the steps below.
Revisions may occur after the delivery date.
implementation
Implementation of the code and the streamlit application
submission
Submission of the code and/or application