Instructions to use jvadlamudi2/convnext-tiny-224-jvadlamudi2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jvadlamudi2/convnext-tiny-224-jvadlamudi2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jvadlamudi2/convnext-tiny-224-jvadlamudi2") 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("jvadlamudi2/convnext-tiny-224-jvadlamudi2") model = AutoModelForImageClassification.from_pretrained("jvadlamudi2/convnext-tiny-224-jvadlamudi2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.6160714285714286, | |
| "eval_loss": 0.6697055697441101, | |
| "eval_runtime": 0.8467, | |
| "eval_samples_per_second": 132.281, | |
| "eval_steps_per_second": 4.724, | |
| "total_flos": 6.716617754447462e+16, | |
| "train_loss": 0.630237170628139, | |
| "train_runtime": 87.2731, | |
| "train_samples_per_second": 30.628, | |
| "train_steps_per_second": 0.241 | |
| } |