Instructions to use aditira/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aditira/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aditira/image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("aditira/image_classification") model = AutoModelForImageClassification.from_pretrained("aditira/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aditira/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/aditira/image_classification/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://aditira/image_classification@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aditira/image_classification/resolve/refs%2Fpr%2F1/training_args.bin
4.09 kB
- Xet hash:
- 2f9b68255226e1af575e4a2249d1b6d932de2d0782bbd1bead368a3a8e06ce78
- Size of remote file:
- 4.09 kB
- SHA256:
- 4e9872fdc80ff68d05054af194a3b1ac98640e82a6ae6a3498d409661f9f6366
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