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https://github.com/towhee-io/examples
Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
https://github.com/towhee-io/examples
audio-classification cross-modal embeddings image-classification machine-learning nlp video-tagging
Last synced: 3 days ago
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Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
- Host: GitHub
- URL: https://github.com/towhee-io/examples
- Owner: towhee-io
- License: apache-2.0
- Created: 2022-04-11T09:48:54.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-02-09T05:43:05.000Z (12 months ago)
- Last Synced: 2025-01-11T20:09:39.030Z (10 days ago)
- Topics: audio-classification, cross-modal, embeddings, image-classification, machine-learning, nlp, video-tagging
- Language: Jupyter Notebook
- Homepage:
- Size: 289 MB
- Stars: 471
- Watchers: 7
- Forks: 115
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Towhee Examples
Towhee Examples are used to analyze the unstructured data with towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
Report Bug or Request Feature
## About Towhee Examples
x2vec, [Towhee](https://github.com/towhee-io/towhee) is all you need! Towhee can generate embedding vectors via a pipeline of ML models and other operations. It aims to make democratize `x2vec`, allowing everyone - from beginner developers to large organizations - to generate dense embeddings with just a few lines of code.
There are many interesting examples that use Towhee to process various unstructured data like images, audio, video, etc. You can easily run these examples on your machine.
## Funny Example List
Bootcamp
Operators
Getting Started
Getting Started with Pipeline
An introduction to `Pipeline`, which can help you better learn the data processing pipeline with Towhee.
Image
Reverse Image Search
Search for images that are similar or related to the input image, it supports a lot of models such as ResNet, VGG, EfficientNet, ViT, etc.
Image Embedding
Timm
Image Animation
Convert an image into an animated image.
Animegan
Cartoongan
Image Deduplication
Find exact or near-exact duplicates within a collection of images.
Image Decode
Timm
Text Image Search
Returns images related to the description of the input query text, which is cross-modal retrieval.
CLIP
Visualization
Under the hood: Embedding models and ANNS indexes in image search.
Image Embedding
NLP
Q&A System
Process user questions and give answers through natural language technology.
Text Embedding
DPR
Search most similar text to the query text across all data.
DPR
Video
Reverse Video Search
It takes a video as input to search for similar videos.
Action Classification
Pytorchvideo
Video Classification
Video Classification is the task of producing a label that is relevant to the video given its frames.
Action Classification
Text Video Search
Search for similar or related videos with the input text.
CLIP4Clip
Deepfake Detection
Predict the probability of a fake video for a given video.
Deepfake
Audio
Audio Classification
Categorize certain sounds into certain categories, such as ambient sound classification and speech recognition.
Audio Classification
Medical
Molecular Search
Search for similar molecular formulas based on the Tanimoto metric, and also supports searching for substructures and superstructures.
RDKit
Data Science
Credit Card Approval Prediction
Predict whether the bank issues a credit card to the applicant, and the credit scores can objectively quantify the magnitude of risk.
Training
Fine Tune
Tutorial about how to fine tuen with towhee.
Image Embedding
## Contributing
Contributions to Milvus Bootcamp are welcome from everyone. See [Guidelines for Contributing](https://github.com/towhee-io/towhee/blob/main/CONTRIBUTING.md) for details.
## Support
Join the Towhee community on [Slack](https://join.slack.com/t/towheeio/shared_invite/zt-19xhoo736-PhIYh~hwOBsDSy5ZvGWJxA) to give feedback, ask for advice, and direct questions to the engineering team. You can also submit [Issues](https://github.com/towhee-io/towhee/issues) or join [Discussions](https://github.com/towhee-io/towhee/discussions).