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Timar

Timar
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reacted to hexgrad's post with 🔥 3 days ago
To Meta AI Research: I would like to fold https://huggingface.co/datasets/ylacombe/expresso into the training mix of an Apache TTS model series. Can you relax the Expresso dataset license to CC-BY or more permissive? Barring that, can I have an individual exception to train on the materials and distribute trained Apache models, without direct redistribution of the original files? Thanks! CC (Expresso paper authors whose handles I could find on HF) @wnhsu @adavirro @bowenshi @itaigat @TalRemez @JadeCopet @hassid @felixkreuk @adiyoss @edupoux
updated a model 8 months ago
Timar/test
reacted to merve's post with ❤️ over 1 year ago
Posting about a very underrated model that tops paperswithcode across different segmentation benchmarks: OneFormer 👑 OneFormer is a "truly universal" model for semantic, instance and panoptic segmentation tasks ⚔️ What makes is truly universal is that it's a single model that is trained only once and can be used across all tasks. The enabler here is the text conditioning, i.e. the model is given a text query that states task type along with the appropriate input, and using contrastive loss, the model learns the difference between different task types 👇 (see in the image below) It's also super easy to use with transformers. ```python from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_large") model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large") # swap the postprocessing and task_inputs for different types of segmentation semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt") semantic_outputs = model(**semantic_inputs) predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] ``` I have drafted a notebook for you to try right away ✨ https://colab.research.google.com/drive/1wfJhoTFqUqcTAYAOUc6TXUubBTmOYaVa?usp=sharing You can also check out the Space without checking out the code itself 👉 https://huggingface.co/spaces/shi-labs/OneFormer
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reacted to hexgrad's post with 🔥 3 days ago
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To Meta AI Research: I would like to fold ylacombe/expresso into the training mix of an Apache TTS model series. Can you relax the Expresso dataset license to CC-BY or more permissive?

Barring that, can I have an individual exception to train on the materials and distribute trained Apache models, without direct redistribution of the original files? Thanks!

CC (Expresso paper authors whose handles I could find on HF) @wnhsu @adavirro @bowenshi @itaigat @TalRemez @JadeCopet @hassid @felixkreuk @adiyoss @edupoux
updated a model 8 months ago
reacted to merve's post with ❤️ over 1 year ago
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Posting about a very underrated model that tops paperswithcode across different segmentation benchmarks: OneFormer 👑

OneFormer is a "truly universal" model for semantic, instance and panoptic segmentation tasks ⚔️
What makes is truly universal is that it's a single model that is trained only once and can be used across all tasks.
The enabler here is the text conditioning, i.e. the model is given a text query that states task type along with the appropriate input, and using contrastive loss, the model learns the difference between different task types 👇 (see in the image below)

It's also super easy to use with transformers. 
from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation

processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")

# swap the postprocessing and task_inputs for different types of segmentation
semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
semantic_outputs = model(**semantic_inputs)
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]

I have drafted a notebook for you to try right away ✨ https://colab.research.google.com/drive/1wfJhoTFqUqcTAYAOUc6TXUubBTmOYaVa?usp=sharing
You can also check out the Space without checking out the code itself 👉 shi-labs/OneFormer
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liked a Space about 2 years ago
New activity in prompthero/openjourney-v4 about 2 years ago

Where to find yaml file for WebUI?

3
#29 opened about 2 years ago by
pocketlim