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")# pip install -U transformers accelerate # 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
Download special_tokens_map.json from mayank0621/owlvit-base-patch32_FT_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 460 Bytes
-
https://huggingface.co/mayank0621/owlvit-base-patch32_FT_cppe5/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://mayank0621/owlvit-base-patch32_FT_cppe5/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/mayank0621/owlvit-base-patch32_FT_cppe5/resolve/main/special_tokens_map.json
460 Bytes
| { | |
| "bos_token": { | |
| "content": "<|startoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "eos_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": "!", | |
| "unk_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |