Instructions to use yangy50/garbage-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yangy50/garbage-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yangy50/garbage-classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("yangy50/garbage-classification") model = AutoModelForImageClassification.from_pretrained("yangy50/garbage-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Yuechen Yang commited on
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Parent(s): e1c3a18
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README.md
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@@ -25,7 +25,7 @@ There are 3 steps to tokenize the image:
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2. Embed each patch with a linear projection
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3. Each embedded patch becomes a token, and the resulting sequence of embedded patches is the sequence you pass to the model.
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I trained the model with 10 epochs, and I use Adam as the optimizer. The accuracy on the test set is
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## Huggingface Space
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Huggingface space is [here](https://huggingface.co/yangy50/garbage-classification).
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2. Embed each patch with a linear projection
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3. Each embedded patch becomes a token, and the resulting sequence of embedded patches is the sequence you pass to the model.
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I trained the model with 10 epochs, and I use Adam as the optimizer. The accuracy on the test set is 95%.
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## Huggingface Space
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Huggingface space is [here](https://huggingface.co/yangy50/garbage-classification).
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