Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ChasingMercer/weather-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChasingMercer/weather-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ChasingMercer/weather-base") 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-base") model = AutoModelForImageClassification.from_pretrained("ChasingMercer/weather-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ChasingMercer/weather-base: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/ChasingMercer/weather-base/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://ChasingMercer/weather-base@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ChasingMercer/weather-base/resolve/refs%2Fpr%2F1/training_args.bin
3.52 kB
- Xet hash:
- 8fe93b51cee25e429973aef98b8289b79683a434f10f644fc60948b8dec3f94e
- Size of remote file:
- 3.52 kB
- SHA256:
- 465c7388a74f286a54712bf921471f74f71cbc8f46aa3b7452041738835f202e
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