Instructions to use Manasee27/cv_animals with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manasee27/cv_animals with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Manasee27/cv_animals") 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("Manasee27/cv_animals") model = AutoModelForImageClassification.from_pretrained("Manasee27/cv_animals", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 4.0, | |
| "global_step": 36, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.11, | |
| "learning_rate": 0.00014444444444444444, | |
| "loss": 0.5734, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 2.22, | |
| "learning_rate": 8.888888888888889e-05, | |
| "loss": 0.1118, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 3.33, | |
| "learning_rate": 3.3333333333333335e-05, | |
| "loss": 0.0578, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "step": 36, | |
| "total_flos": 4.029619575840768e+16, | |
| "train_loss": 0.21459072166019016, | |
| "train_runtime": 99.217, | |
| "train_samples_per_second": 5.241, | |
| "train_steps_per_second": 0.363 | |
| } | |
| ], | |
| "max_steps": 36, | |
| "num_train_epochs": 4, | |
| "total_flos": 4.029619575840768e+16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |