Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use jcm-art/hf_image_classification_tuning_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcm-art/hf_image_classification_tuning_pipeline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jcm-art/hf_image_classification_tuning_pipeline") 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("jcm-art/hf_image_classification_tuning_pipeline") model = AutoModelForImageClassification.from_pretrained("jcm-art/hf_image_classification_tuning_pipeline", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28999e1140551d67e6a835701a15d9e98e1dfc9e2f209c0b00cfd2bf9700bf70
- Size of remote file:
- 344 MB
- SHA256:
- a6f6f3c6f5ef000520a1fe096eec2e1e0f5e82e23f4ac80fcefe67e1448d7161
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