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:
- ad31dbfe464cb581ee4e26549b177bdc7fe7b879592ebcadb6ae78ae1a485d31
- Size of remote file:
- 4.03 kB
- SHA256:
- 740089ae7a43af38acd030206cbdb64354f3726267c626e096e84b9711372e13
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