Zero-Shot Image Classification
Transformers
English
medical
multimodal
vision-language pre-training
chest x-ray
Instructions to use pykale/MeDSLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pykale/MeDSLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="pykale/MeDSLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pykale/MeDSLIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download PreTrain_MeDSLIP/optim/__init__.py from pykale/MeDSLIP: direct link, hf CLI and curl.
- Browser
- Download file 369 Bytes
-
https://huggingface.co/pykale/MeDSLIP/resolve/refs%2Fpr%2F1/PreTrain_MeDSLIP/optim/__init__.py
- Command line
-
hf download hf://pykale/MeDSLIP@refs/pr/1/PreTrain_MeDSLIP/optim/__init__.py
-
curl -L -o __init__.py https://huggingface.co/pykale/MeDSLIP/resolve/refs%2Fpr%2F1/PreTrain_MeDSLIP/optim/__init__.py
369 Bytes
| from .adamp import AdamP | |
| from .adamw import AdamW | |
| from .adafactor import Adafactor | |
| from .adahessian import Adahessian | |
| from .lookahead import Lookahead | |
| from .nadam import Nadam | |
| from .novograd import NovoGrad | |
| from .nvnovograd import NvNovoGrad | |
| from .radam import RAdam | |
| from .rmsprop_tf import RMSpropTF | |
| from .sgdp import SGDP | |
| from .optim_factory import create_optimizer | |