https://github.com/spidy20/landmark_detection
Landmarks Recogntion Web application using Streamlit.
https://github.com/spidy20/landmark_detection
landmark-detection landmark-recognition python streamlit
Last synced: about 2 months ago
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Landmarks Recogntion Web application using Streamlit.
- Host: GitHub
- URL: https://github.com/spidy20/landmark_detection
- Owner: Spidy20
- Created: 2021-12-24T12:17:25.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-12-24T13:16:30.000Z (over 3 years ago)
- Last Synced: 2025-03-26T11:44:44.587Z (3 months ago)
- Topics: landmark-detection, landmark-recognition, python, streamlit
- Language: Python
- Homepage:
- Size: 1.57 MB
- Stars: 12
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Landmark Recognition Web-App using Streamlit
[](https://www.python.org/)
[](https://www.python.org/downloads/release/python-360/)## [Watch Tutorial for this project](https://youtu.be/Jq9iVoir55g)
## Source
- Trained model [`landmarks_classifier_asia_V1/1`](https://tfhub.dev/google/on_device_vision/classifier/landmarks_classifier_asia_V1/1) is taken from the Tensorflow-Hub
- There are total `98961` classes supported, in which Asia's most of the famous Landmark is covered.
- This model was trained on [`Google Landmarks Dataset V2`](https://ai.googleblog.com/2019/05/announcing-google-landmarks-v2-improved.html).## Features
- Simple repsonsive UI.
- It will give you the full address of Landmark
- It will provide you the `Latitude` & `Longitude` of predicted landmark.
- It will plot the predicted landmark on the Map.## Usage
- Clone my repository.
- Open CMD in working directory.
- Run following command.```
pip install -r requirements.txt
```
- `LM_Detection.py` is the main Python file of Streamlit Web-Application.
- To run app, write following command in CMD. or use any IDE.```
streamlit run LM_Detection.py
```- For more explanation of this project see the tutorial on Machine Learning Hub YouTube channel.
## Screenshots
![]()
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