Image Classification
Transformers
LiteRT
ONNX
Safetensors
English
siglip
zero-shot-image-classification
vision
cervical-cancer
diagnosis
Instructions to use KhanyiTapiwa00/medsiglip-diagnosis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KhanyiTapiwa00/medsiglip-diagnosis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="KhanyiTapiwa00/medsiglip-diagnosis") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("KhanyiTapiwa00/medsiglip-diagnosis") model = AutoModelForZeroShotImageClassification.from_pretrained("KhanyiTapiwa00/medsiglip-diagnosis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eos_token": { | |
| "content": "</s>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "</s>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "<unk>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false | |
| } | |
| } | |