Instructions to use frncscp/patacoptimus-prime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frncscp/patacoptimus-prime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frncscp/patacoptimus-prime") 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("frncscp/patacoptimus-prime") model = AutoModelForImageClassification.from_pretrained("frncscp/patacoptimus-prime", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: frncscp/patacoptimus-prime | |
| results: [] | |
| datasets: | |
| - frncscp/patacon-730 | |
| metrics: | |
| - accuracy | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # frncscp/patacoptimus-prime | |
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on [frncscp/patacon-730](https://huggingface.co/datasets/frncscp/patacon-730). | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.0043 | |
| - Validation Loss: 0.0086 | |
| - Train Accuracy: 0.9977 | |
| - Epoch: 14 | |
| ## Model description | |
| One-Class Patacognition Transformer | |
| ## Intended uses & limitations | |
| It was designed for One-Class Patacón Classification | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 34960, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Validation Loss | Train Accuracy | Epoch | | |
| |:----------:|:---------------:|:--------------:|:-----:| | |
| | 0.0064 | 0.0079 | 0.9977 | 0 | | |
| | 0.0043 | 0.0086 | 0.9977 | 1 | | |
| ### Framework versions | |
| - Transformers 4.28.1 | |
| - TensorFlow 2.12.0 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 |