Instructions to use dima806/facial_age_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/facial_age_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/facial_age_image_detection") 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("dima806/facial_age_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/facial_age_image_detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download checkpoint-6400/training_args.bin from dima806/facial_age_image_detection: direct link, hf CLI and curl.
- Browser
- Download file 4.41 kB
-
https://huggingface.co/dima806/facial_age_image_detection/resolve/main/checkpoint-6400/training_args.bin
- Command line
-
hf download hf://dima806/facial_age_image_detection/checkpoint-6400/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dima806/facial_age_image_detection/resolve/main/checkpoint-6400/training_args.bin
4.41 kB
- Xet hash:
- c06a63452afb9e5c9cf934a3205e471eaba4a23fbed0894579e7f05074d014cc
- Size of remote file:
- 4.41 kB
- SHA256:
- 9f6a197bb61538af9105ff3f98f5f8f19f1a1b0dc25898782642a1539f61d8e6
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