Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/vit-base-patch16-224-in21k_Simpsons_Family_Members 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_Simpsons_Family_Members 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_Simpsons_Family_Members") 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_Simpsons_Family_Members") model = AutoModelForImageClassification.from_pretrained("DunnBC22/vit-base-patch16-224-in21k_Simpsons_Family_Members") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_Macro F1": 0.9521097000368888, | |
| "eval_Macro Precision": 0.9601291764998138, | |
| "eval_Macro Recall": 0.9530864197530864, | |
| "eval_Micro F1": 0.9529702970297029, | |
| "eval_Micro Precision": 0.9529702970297029, | |
| "eval_Micro Recall": 0.9529702970297029, | |
| "eval_Weighted F1": 0.9522288953284159, | |
| "eval_Weighted Precision": 0.9604642926098597, | |
| "eval_Weighted Recall": 0.9529702970297029, | |
| "eval_accuracy": 0.9529702970297029, | |
| "eval_loss": 0.2431316375732422, | |
| "eval_runtime": 360.2147, | |
| "eval_samples_per_second": 1.122, | |
| "eval_steps_per_second": 0.142, | |
| "train_loss": 0.08599743702456394, | |
| "train_runtime": 48467.025, | |
| "train_samples_per_second": 0.369, | |
| "train_steps_per_second": 0.023 | |
| } |