Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/vit-base-patch16-224-in21k-weather-images-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/vit-base-patch16-224-in21k-weather-images-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DunnBC22/vit-base-patch16-224-in21k-weather-images-classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("DunnBC22/vit-base-patch16-224-in21k-weather-images-classification") model = AutoModelForImageClassification.from_pretrained("DunnBC22/vit-base-patch16-224-in21k-weather-images-classification") - Notebooks
- Google Colab
- Kaggle
File size: 614 Bytes
7d17483 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"epoch": 3.0,
"eval_Macro F1": 0.9372182266715665,
"eval_Macro Precision": 0.9397527643161172,
"eval_Macro Recall": 0.9353770361300282,
"eval_Micro F1": 0.9339762611275965,
"eval_Micro Precision": 0.9339762611275965,
"eval_Micro Recall": 0.9339762611275965,
"eval_Weighted F1": 0.9340639641016528,
"eval_Weighted Precision": 0.9346802003761767,
"eval_Weighted Recall": 0.9339762611275965,
"eval_accuracy": 0.9339762611275965,
"eval_loss": 0.2254980355501175,
"eval_runtime": 1167.1078,
"eval_samples_per_second": 1.155,
"eval_steps_per_second": 0.145
} |