Instructions to use Suru/FruitModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Suru/FruitModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Suru/FruitModel") 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("Suru/FruitModel") model = AutoModelForImageClassification.from_pretrained("Suru/FruitModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 4.0, | |
| "global_step": 32, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.25, | |
| "learning_rate": 0.0001375, | |
| "loss": 0.3558, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 2.5, | |
| "learning_rate": 7.500000000000001e-05, | |
| "loss": 0.0542, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 3.75, | |
| "learning_rate": 1.25e-05, | |
| "loss": 0.025, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "step": 32, | |
| "total_flos": 3.657621909808742e+16, | |
| "train_loss": 0.13729210989549756, | |
| "train_runtime": 38.8164, | |
| "train_samples_per_second": 12.16, | |
| "train_steps_per_second": 0.824 | |
| } | |
| ], | |
| "max_steps": 32, | |
| "num_train_epochs": 4, | |
| "total_flos": 3.657621909808742e+16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |