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 pytorch_model.bin from ChasingMercer/weather-base: direct link, hf CLI and curl.
- Browser
- Download file 347 MB
-
https://huggingface.co/ChasingMercer/weather-base/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ChasingMercer/weather-base@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ChasingMercer/weather-base/resolve/refs%2Fpr%2F1/pytorch_model.bin
347 MB
- Xet hash:
- a6d96bb1ea71d8f3763752a9ea0e76034300dca33c1396ec1c774c718bd43f9c
- Size of remote file:
- 347 MB
- SHA256:
- 4a5a4732f94dd6ea669e29df67dd5a0ab50e6902336d4ea5f61aaf4967708ac2
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