Instructions to use google/owlv2-base-patch16-ensemble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/owlv2-base-patch16-ensemble with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="google/owlv2-base-patch16-ensemble")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") model = AutoModelForZeroShotObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from google/owlv2-base-patch16-ensemble: direct link, hf CLI and curl.
- Browser
- Download file 414 Bytes
-
https://huggingface.co/google/owlv2-base-patch16-ensemble/resolve/refs%2Fpr%2F5/config.json
- Command line
-
hf download hf://google/owlv2-base-patch16-ensemble@refs/pr/5/config.json
-
curl -L -o config.json https://huggingface.co/google/owlv2-base-patch16-ensemble/resolve/refs%2Fpr%2F5/config.json
414 Bytes
| { | |
| "architectures": [ | |
| "Owlv2ForObjectDetection" | |
| ], | |
| "initializer_factor": 1.0, | |
| "logit_scale_init_value": 2.6592, | |
| "model_type": "owlv2", | |
| "projection_dim": 512, | |
| "text_config": { | |
| "model_type": "owlv2_text_model" | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.35.0.dev0", | |
| "vision_config": { | |
| "image_size": 960, | |
| "model_type": "owlv2_vision_model", | |
| "patch_size": 16 | |
| } | |
| } | |