Add pipeline tag and library name, improve description
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by
nielsr
HF staff
- opened
README.md
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license: mit
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---
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<h2>[Installation Free!] Quicker Start with Hugging Face AutoModel</h2>
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Do the image quality interpreting chat with q-sit.
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```python
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import requests
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from PIL import Image
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# very low
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```
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```python
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import torch
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import requests
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Assume you are an image quality evaluator.
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{"type": "image"},
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],
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},
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# if you want range from 0-5, multiply 5
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```
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---
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license: mit
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pipeline_tag: image-to-text
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library_name: transformers
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---
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# Q-SiT: Image Quality Scoring and Interpreting with Large Language Models
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Q-SiT is a model for image quality scoring and interpretation. It uses a Large Language Model to perform both tasks simultaneously, recognizing the inherent connection between perception and decision-making in the human visual system. Unlike previous approaches which treat scoring and interpreting as separate tasks, Q-SiT provides a unified framework.
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Project page: https://github.com/Q-Future/Q-SiT
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## Quicker Start with Hugging Face AutoModel
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No need to install this GitHub repo. Ensure that you use the Transformers package version 4.45.0 (`pip install transformers==4.45.0`).
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### Image Quality Interpreting Chat
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```python
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import requests
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from PIL import Image
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# very low
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```
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### Image Quality Scoring
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```python
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import torch
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import requests
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Assume you are an image quality evaluator.
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Your rating should be chosen from the following five categories: Excellent, Good, Fair, Poor, and Bad (from high to low).
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How would you rate the quality of this image?"},
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{"type": "image"},
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],
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},
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# if you want range from 0-5, multiply 5
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```
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For dataset evaluation scripts, please refer to [this directory](https://github.com/Q-Future/Q-SiT/tree/main/eval_scripts). For training information, see the [Training Q-SiT](https://github.com/Q-Future/Q-SiT#training-q-sit) section of the GitHub repository.
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## Citation
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To do
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