Instructions to use sj21867/ai_art_exp3_efficientnetb3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sj21867/ai_art_exp3_efficientnetb3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sj21867/ai_art_exp3_efficientnetb3") 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("sj21867/ai_art_exp3_efficientnetb3") model = AutoModelForImageClassification.from_pretrained("sj21867/ai_art_exp3_efficientnetb3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sj21867/ai_art_exp3_efficientnetb3: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/sj21867/ai_art_exp3_efficientnetb3/resolve/main/training_args.bin
- Command line
-
hf download hf://sj21867/ai_art_exp3_efficientnetb3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sj21867/ai_art_exp3_efficientnetb3/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- bcdc96142a5f31076dae8c5aa5d44ba63dc095e24e30ee0ebbc0b4cfb6706c20
- Size of remote file:
- 5.18 kB
- SHA256:
- 1b17b566b627c24ec09a629350cb428160c7f9419abf3a35b2c83c6e9203f0f2
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