Image-to-Image
Transformers
low-light
low-light-image-enhancement
image-enhancement
image-restoration
computer-vision
low-light-enhance
multimodal
multimodal-learning
transformer
vision-transformer
vision-transformers
Eval Results (legacy)
Instructions to use albrateanu/ModalFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use albrateanu/ModalFormer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-image" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-image", model="albrateanu/ModalFormer")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("albrateanu/ModalFormer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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- name: SSIM
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type: SSIM
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value: 0.904
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---
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# ✨ ModalFormer: Multimodal Transformer for Low-Light Image Enhancement
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- name: SSIM
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type: SSIM
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value: 0.904
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- task:
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type: low-light-image-enhancement
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dataset:
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name: MEF
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type: MEF
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metrics:
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- name: NIQE
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type: NIQE
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value: 3.44
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- task:
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type: low-light-image-enhancement
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dataset:
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name: LIME
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type: LIME
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metrics:
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- name: NIQE
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type: NIQE
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value: 3.82
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- task:
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type: low-light-image-enhancement
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dataset:
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name: DICM
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type: DICM
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metrics:
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- name: NIQE
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type: NIQE
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value: 3.64
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- task:
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type: low-light-image-enhancement
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dataset:
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name: NPE
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type: NPE
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metrics:
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- name: NIQE
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type: NIQE
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value: 3.55
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---
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# ✨ ModalFormer: Multimodal Transformer for Low-Light Image Enhancement
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