Instructions to use aspends/coco_binary_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aspends/coco_binary_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aspends/coco_binary_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("aspends/coco_binary_classification") model = AutoModelForImageClassification.from_pretrained("aspends/coco_binary_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from aspends/coco_binary_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/aspends/coco_binary_classification/resolve/main/tf_model.h5
- Command line
-
hf download hf://aspends/coco_binary_classification/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/aspends/coco_binary_classification/resolve/main/tf_model.h5
343 MB
- Xet hash:
- 16e46f33280ca84e7bcab2d430c5813268bd517ff5a1d47778f303c484760486
- Size of remote file:
- 343 MB
- SHA256:
- 31bcd333904b8f92e39b95c029b1a966d8d22fbe638d926692b4cef8e9f5aade
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