Feature Extraction
Transformers
PyTorch
clip
zero-shot-image-classification
vision
coin
coin-retrieval
coin-recognition
coin-search-engine
multi-modal learning
Instructions to use breezedeus/coin-clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use breezedeus/coin-clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="breezedeus/coin-clip-vit-base-patch32")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("breezedeus/coin-clip-vit-base-patch32") model = AutoModelForZeroShotImageClassification.from_pretrained("breezedeus/coin-clip-vit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): c3e0d53
Update README.md
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README.md
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@@ -77,6 +77,7 @@ More examples can be found: [breezedeus/Coin-CLIP: Coin CLIP](https://github.com
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from PIL import Image
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import requests
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from transformers import CLIPProcessor, CLIPModel
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model = CLIPModel.from_pretrained("breezedeus/coin-clip-vit-base-patch32")
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from PIL import Image
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import requests
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import torch.nn.functional as F
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from transformers import CLIPProcessor, CLIPModel
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model = CLIPModel.from_pretrained("breezedeus/coin-clip-vit-base-patch32")
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