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 train_results.json from ChasingMercer/weather-base: direct link, hf CLI and curl.
- Browser
- Download file 207 Bytes
-
https://huggingface.co/ChasingMercer/weather-base/resolve/main/train_results.json
- Command line
-
hf download hf://ChasingMercer/weather-base/train_results.json
-
curl -L -o train_results.json https://huggingface.co/ChasingMercer/weather-base/resolve/main/train_results.json
207 Bytes
| { | |
| "epoch": 6.0, | |
| "total_flos": 2.549919337377528e+18, | |
| "train_loss": 0.2920494159759834, | |
| "train_runtime": 2402.1234, | |
| "train_samples_per_second": 13.71, | |
| "train_steps_per_second": 0.427 | |
| } |