Instructions to use HorcruxNo13/swin-tiny-patch4-window7-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HorcruxNo13/swin-tiny-patch4-window7-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HorcruxNo13/swin-tiny-patch4-window7-224") 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("HorcruxNo13/swin-tiny-patch4-window7-224") model = AutoModelForImageClassification.from_pretrained("HorcruxNo13/swin-tiny-patch4-window7-224") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.7375, | |
| "best_model_checkpoint": "swin-tiny-patch4-window7-224/checkpoint-24", | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 24, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.7333333333333333, | |
| "eval_loss": 0.5740450024604797, | |
| "eval_runtime": 1.7884, | |
| "eval_samples_per_second": 134.201, | |
| "eval_steps_per_second": 4.473, | |
| "step": 8 | |
| }, | |
| { | |
| "epoch": 1.25, | |
| "learning_rate": 3.3333333333333335e-05, | |
| "loss": 0.6033, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.7333333333333333, | |
| "eval_loss": 0.5640280246734619, | |
| "eval_runtime": 1.8333, | |
| "eval_samples_per_second": 130.909, | |
| "eval_steps_per_second": 4.364, | |
| "step": 16 | |
| }, | |
| { | |
| "epoch": 2.5, | |
| "learning_rate": 9.523809523809523e-06, | |
| "loss": 0.5751, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.7375, | |
| "eval_loss": 0.5567867159843445, | |
| "eval_runtime": 1.9734, | |
| "eval_samples_per_second": 121.615, | |
| "eval_steps_per_second": 4.054, | |
| "step": 24 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "step": 24, | |
| "total_flos": 7.4567966957568e+16, | |
| "train_loss": 0.5813349982102712, | |
| "train_runtime": 56.867, | |
| "train_samples_per_second": 52.755, | |
| "train_steps_per_second": 0.422 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.73, | |
| "eval_loss": 0.5407183766365051, | |
| "eval_runtime": 70.3286, | |
| "eval_samples_per_second": 4.266, | |
| "eval_steps_per_second": 0.142, | |
| "step": 24 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 24, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "total_flos": 7.4567966957568e+16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |