https://github.com/taquynhnga2001/cnns-interpretation-visualization
Visualise computer vision models (ResNet, MobileNet, ConvNext) with visual explanation methods in PyTorch
https://github.com/taquynhnga2001/cnns-interpretation-visualization
artificial-intelligence computer-vision deep-learning machine-learning neural-network streamlit
Last synced: about 2 months ago
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Visualise computer vision models (ResNet, MobileNet, ConvNext) with visual explanation methods in PyTorch
- Host: GitHub
- URL: https://github.com/taquynhnga2001/cnns-interpretation-visualization
- Owner: taquynhnga2001
- Created: 2022-12-08T19:16:07.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-02-01T13:25:08.000Z (over 2 years ago)
- Last Synced: 2025-03-27T21:34:03.297Z (about 1 year ago)
- Topics: artificial-intelligence, computer-vision, deep-learning, machine-learning, neural-network, streamlit
- Language: Python
- Homepage:
- Size: 21.7 MB
- Stars: 3
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
---
title: CNNs Interpretation Visualization
emoji: 💡
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.10.0
app_file: Home.py
pinned: false
---
# Visualizing Interpretations of CNN models: ConvNeXt, ResNet and MobileNet
To be change name: CNNs-interpretation-visualization
This app was built with Streamlit. To run the app, `streamlit run Home.py` in the terminal.
This repo lacks one more folder `data/preprocessed_image_net` which contains 50,000 preprocessed imagenet validation images saved in 5 pickle files.
# Featured on NTU SCSE Technovation 2023
- Poster: https://www.ntu.edu.sg/docs/librariesprovider118/technovationposter/apr2023/ta-quynh-nga_visualizing-interpretations-of-deep-neural-networks.pdf?sfvrsn=a920af4b_3
- Video demo: https://www.youtube.com/watch?v=eBtrV5wHYos&ab_channel=SCSEMarketing
- Read report here: https://dr.ntu.edu.sg/handle/10356/166663