Image Feature Extraction
Transformers
Safetensors
dinov2
dino
vision
image-embeddings
pet-recognition
Instructions to use bcd8697/trial-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bcd8697/trial-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="bcd8697/trial-model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("bcd8697/trial-model") model = AutoModel.from_pretrained("bcd8697/trial-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 658471824b04e438a97c0584d6ef7f280b33103497d6b4f6c874eb27cdb18cf4
- Size of remote file:
- 164 MB
- SHA256:
- 5b13108cf8cc440be6b5c19fd0f517b2f8705a80430c644a7d461b1ed8662fb5
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