Instructions to use agestau/fashion_classification_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agestau/fashion_classification_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="agestau/fashion_classification_2") 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("agestau/fashion_classification_2") model = AutoModelForImageClassification.from_pretrained("agestau/fashion_classification_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 649a799d7d1cc8034e2807fb3ddd013e650adbf73ee37d8294d5fea5086be77c
- Size of remote file:
- 110 MB
- SHA256:
- 4528667cc69f2d2a3292ce9591ffe3827bad9765adbd2d8b2d585aef51ae3c8f
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