Instructions to use jacobthebanana/vit-recycling-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacobthebanana/vit-recycling-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jacobthebanana/vit-recycling-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("jacobthebanana/vit-recycling-classification") model = AutoModelForImageClassification.from_pretrained("jacobthebanana/vit-recycling-classification") - Notebooks
- Google Colab
- Kaggle
Technical details:
- Base model:
google/vit-base-patch16-224 - Dataset: [Recycling Classification (12 classes)] (https://www.kaggle.com/datasets/mostafaabla/garbage-classification)
- Learning rate: 0.0001
- Effective training batch size: 16 (2 items per TPU core over 8 cores.)
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
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