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 all_results.json from ChasingMercer/weather-mod: direct link, hf CLI and curl.
- Browser
- Download file 210 Bytes
-
https://huggingface.co/ChasingMercer/weather-mod/resolve/main/all_results.json
- Command line
-
hf download hf://ChasingMercer/weather-mod/all_results.json
-
curl -L -o all_results.json https://huggingface.co/ChasingMercer/weather-mod/resolve/main/all_results.json
210 Bytes
| { | |
| "epoch": 6.0, | |
| "total_flos": 1.7589230025365053e+18, | |
| "train_loss": 0.10344488720941004, | |
| "train_runtime": 2153.5363, | |
| "train_samples_per_second": 10.506, | |
| "train_steps_per_second": 0.329 | |
| } |