Instructions to use ThatOrJohn/efficientnet-b1-pineapple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThatOrJohn/efficientnet-b1-pineapple with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ThatOrJohn/efficientnet-b1-pineapple") 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("ThatOrJohn/efficientnet-b1-pineapple") model = AutoModelForImageClassification.from_pretrained("ThatOrJohn/efficientnet-b1-pineapple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "data_path": "ThatOrJohn/pineapple", | |
| "model": "google/efficientnet-b1", | |
| "username": "ThatOrJohn", | |
| "lr": 5e-05, | |
| "epochs": 4, | |
| "batch_size": 8, | |
| "warmup_ratio": 0.1, | |
| "gradient_accumulation": 1, | |
| "optimizer": "adamw_torch", | |
| "scheduler": "linear", | |
| "weight_decay": 0.0, | |
| "max_grad_norm": 1.0, | |
| "seed": 42, | |
| "train_split": "train", | |
| "valid_split": null, | |
| "logging_steps": -1, | |
| "project_name": "efficientnet-b1-pineapple", | |
| "auto_find_batch_size": false, | |
| "mixed_precision": "none", | |
| "save_total_limit": 1, | |
| "push_to_hub": true, | |
| "evaluation_strategy": "epoch", | |
| "image_column": "image", | |
| "target_column": "label", | |
| "log": "tensorboard", | |
| "early_stopping_patience": 5, | |
| "early_stopping_threshold": 0.01 | |
| } |