Instructions to use jnmrr/ds3-img-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jnmrr/ds3-img-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jnmrr/ds3-img-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("jnmrr/ds3-img-classification") model = AutoModelForImageClassification.from_pretrained("jnmrr/ds3-img-classification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eval_accuracy": 0.15015097447158934, | |
| "eval_loss": 4.886590003967285, | |
| "eval_macro_f1": 0.033358390849091486, | |
| "eval_macro_precision": 0.039403090337421884, | |
| "eval_macro_recall": 0.07102345263438722, | |
| "eval_micro_f1": 0.15015097447158934, | |
| "eval_micro_precision": 0.15015097447158934, | |
| "eval_micro_recall": 0.15015097447158934, | |
| "eval_runtime": 10.4546, | |
| "eval_samples_per_second": 348.46, | |
| "eval_steps_per_second": 0.765, | |
| "eval_weighted_f1": 0.054572202456524575, | |
| "eval_weighted_precision": 0.06415751425956755, | |
| "eval_weighted_recall": 0.15015097447158934 | |
| } |