Instructions to use ppicazo/moon-detector-v5.c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ppicazo/moon-detector-v5.c with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ppicazo/moon-detector-v5.c") 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("ppicazo/moon-detector-v5.c") model = AutoModelForImageClassification.from_pretrained("ppicazo/moon-detector-v5.c", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
Verified signature- Adding `safetensors` variant of this model (5a67231ba1856f81c7f4f11c5645f5e9d2698a3b) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>ccdb27a
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Model save
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Training in progress, epoch 5
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Training in progress, epoch 4
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Training in progress, epoch 3
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Training in progress, epoch 2
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Training in progress, epoch 1
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initial commit
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