Instructions to use umm-maybe/AI-image-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use umm-maybe/AI-image-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="umm-maybe/AI-image-detector") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("umm-maybe/AI-image-detector", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from umm-maybe/AI-image-detector: direct link, hf CLI and curl.
- Browser
- Download file 240 Bytes
-
https://huggingface.co/umm-maybe/AI-image-detector/resolve/refs%2Fpr%2F2/preprocessor_config.json
- Command line
-
hf download hf://umm-maybe/AI-image-detector@refs/pr/2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/umm-maybe/AI-image-detector/resolve/refs%2Fpr%2F2/preprocessor_config.json
240 Bytes
- Xet hash:
- 0bf9083f09ef515a0e3552b9787af958e8388c8bb279cd47e01d1647dfc8d30f
- Size of remote file:
- 240 Bytes
- SHA256:
- 63ae741f85ba4dfcb1917ef808968baaad3a395dd8067e970c872d0e6ac05f3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.