Updated: Jun 8, 2023
1-Click Kit 
Image segmentation is the task of partitioning an image based on the objects present and their semantic importance. Image Segmentation divides an image into segments where each pixel in the image is mapped to an object.
In object detection, objects are often represented by bounding boxes, which are like drawing a rectangle around the object. These rectangles give a general idea of the object's location, but they don't show the exact shape of the object. They may also include parts of the background or other objects inside the rectangle, making it difficult to separate objects from their surroundings.
Segmentation masks, on the other hand, are like drawing a detailed outline around the object, following its exact shape. This allows for a more precise understanding of the object's shape, size, and position.
In this kit, we will make use of Meta AI's Segment Anything Model (SAM). The model was trained on a dataset consisting of 11 million images and more than a billion segmentation masks. Also, we make use of the OWL-ViT (short for Vision Transformer for Open-World Localization) which is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries.
Deployment Information
For Windows OS,
- Download, extract the zip file and run. Do ensure to extract the zip file before running it.
- After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook.
- To run the kit manually, press 'N' and locate the zip file 'image_restoration'.zip'.
- Extract the zip file and navigate to the directory 'image_segmentation'.
- Open command prompt in the extracted directory 'image_segmentation' and run the command 'jupyter notebook'
- Locate and open the 'Image_Segmentation.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook.
For other Operating Systems,
- Click here to download the repository.
- Extract the zip file and navigate to the directory image_segmentation.zip
- Extract the zip file and navigate to the directory 'image_segmentation'.
- Open command prompt in the extracted directory 'image_segmentation' and run the command 'jupyter notebook'
- Locate and open the 'Image_Segmentation.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook.
Click the button below to download the solution and follow the deployment information to begin set-up. This 1-click kit has all the required dependencies and resources to build your Image Restoration Engine.
Libraries used in this solution
Development Environment
VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.
Jupyter Notebook is used for our development.
jupyterby jupyter
14404 Version:CurrentJupyter metapackage for installation, docs and chat
jupyterby jupyter
Python 14404 Version:Current License: Permissive (BSD-3-Clause)
Machine Learning
Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning
pytorchby pytorch
67874 Version:v2.0.1Tensors and Dynamic neural networks in Python with strong GPU acceleration
pytorchby pytorch
Python 67874 Version:v2.0.1 License: Others (Non-SPDX)
opencv-pythonby opencv
3491 Version:72Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages.
opencv-pythonby opencv
Shell 3491 Version:72 License: Permissive (MIT)
segment-anythingby facebookresearch
34688 Version:CurrentThe repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
segment-anythingby facebookresearch
Jupyter Notebook 34688 Version:Current License: Permissive (Apache-2.0)
transformersby huggingface
104111 Version:v4.30.2🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
transformersby huggingface
Python 104111 Version:v4.30.2 License: Permissive (Apache-2.0)
Kit Solution Source
image_segmentationby kandi1clickkits
0 Version:v1.0.0Image Segmentation using Meta AI's Segment Anything Model (SAM)
image_segmentationby kandi1clickkits
Jupyter Notebook 0 Version:v1.0.0 License: Permissive (Apache-2.0)
App User Interface
gradioby gradio-app
18771 Version:v3.35.2Create UIs for your machine learning model in Python in 3 minutes
gradioby gradio-app
Python 18771 Version:v3.35.2 License: Permissive (Apache-2.0)