Instructions to use adilsaleem/ecg-image-multilabel-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adilsaleem/ecg-image-multilabel-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="adilsaleem/ecg-image-multilabel-classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("adilsaleem/ecg-image-multilabel-classification") model = AutoModelForImageClassification.from_pretrained("adilsaleem/ecg-image-multilabel-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from adilsaleem/ecg-image-multilabel-classification: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/adilsaleem/ecg-image-multilabel-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://adilsaleem/ecg-image-multilabel-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/adilsaleem/ecg-image-multilabel-classification/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 09dc28a4110b6309ebfab7ece56e9e3c5645d6ff1a935a0c8dabf9a9355710ba
- Size of remote file:
- 4.6 kB
- SHA256:
- ce0f27112f4b0ff4e276a54d09ce5b41e912c07de2bad95b64db9402831cb25f
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