Instructions to use Anthony-Pulla/platzi-practica with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anthony-Pulla/platzi-practica with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Anthony-Pulla/platzi-practica") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Anthony-Pulla/platzi-practica") model = AutoModelForImageClassification.from_pretrained("Anthony-Pulla/platzi-practica", device_map="auto") - Notebooks
- Google Colab
- Kaggle
platzi-practica
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0002
- Accuracy: 1.0
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0007 | 1.6 | 500 | 0.0004 | 1.0 |
| 0.0003 | 3.19 | 1000 | 0.0003 | 1.0 |
| 0.0002 | 4.79 | 1500 | 0.0002 | 1.0 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for Anthony-Pulla/platzi-practica
Base model
google/vit-base-patch16-224-in21k