Instructions to use RavenK/TAC-ViT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RavenK/TAC-ViT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RavenK/TAC-ViT-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RavenK/TAC-ViT-base") model = AutoModel.from_pretrained("RavenK/TAC-ViT-base") - Notebooks
- Google Colab
- Kaggle
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README.md
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This model is used for encoding a depth image into a dense feature.
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## Model Details
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### Model Description
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This model is used for encoding a depth image into a dense feature.
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**Caution,** the model does not contain the last FC layer.
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So, the output features are not aligned with RGB.
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## Model Details
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### Model Description
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