Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ChasingMercer/weather-mod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChasingMercer/weather-mod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ChasingMercer/weather-mod") 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("ChasingMercer/weather-mod") model = AutoModelForImageClassification.from_pretrained("ChasingMercer/weather-mod", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ChasingMercer/weather-mod: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/ChasingMercer/weather-mod/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://ChasingMercer/weather-mod@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ChasingMercer/weather-mod/resolve/refs%2Fpr%2F1/training_args.bin
3.52 kB
- Xet hash:
- 32a4a20c6853f99de54fff5406c0ec2b655a8a4d4b27c74c87eefa325afbf011
- Size of remote file:
- 3.52 kB
- SHA256:
- 08da4e18c479ad36fc4203e76ba6c0d1465c8208176b534a260353a8e82a76b4
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