Instructions to use mayank0621/owlvit-base-patch32_FT_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayank0621/owlvit-base-patch32_FT_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="mayank0621/owlvit-base-patch32_FT_cppe5")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("mayank0621/owlvit-base-patch32_FT_cppe5") model = AutoModelForZeroShotObjectDetection.from_pretrained("mayank0621/owlvit-base-patch32_FT_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "google/owlvit-base-patch32", | |
| "architectures": [ | |
| "OwlViTForObjectDetection" | |
| ], | |
| "id2label": { | |
| "0": "Coverall", | |
| "1": "Face_Shield", | |
| "2": "Gloves", | |
| "3": "Goggles", | |
| "4": "Mask" | |
| }, | |
| "initializer_factor": 1.0, | |
| "label2id": { | |
| "Coverall": 0, | |
| "Face_Shield": 1, | |
| "Gloves": 2, | |
| "Goggles": 3, | |
| "Mask": 4 | |
| }, | |
| "logit_scale_init_value": 2.6592, | |
| "model_type": "owlvit", | |
| "projection_dim": 512, | |
| "text_config": { | |
| "bos_token_id": 0, | |
| "dropout": 0.0, | |
| "eos_token_id": 2, | |
| "max_length": 16, | |
| "model_type": "owlvit_text_model", | |
| "pad_token_id": 1 | |
| }, | |
| "text_config_dict": null, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.41.2", | |
| "vision_config": { | |
| "dropout": 0.0, | |
| "model_type": "owlvit_vision_model" | |
| }, | |
| "vision_config_dict": null | |
| } | |