Instructions to use Kibalama/Digit_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kibalama/Digit_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kibalama/Digit_classification_model") 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("Kibalama/Digit_classification_model") model = AutoModelForImageClassification.from_pretrained("Kibalama/Digit_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Kibalama/Digit_classification_model: direct link, hf CLI and curl.
- Browser
- Download file 1.75 kB
-
https://huggingface.co/Kibalama/Digit_classification_model/resolve/main/README.md
- Command line
-
hf download hf://Kibalama/Digit_classification_model/README.md
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curl -L -o README.md https://huggingface.co/Kibalama/Digit_classification_model/resolve/main/README.md
1.75 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google/vit-base-patch16-224-in21k | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Digit_classification_model | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Digit_classification_model | |
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2617 | |
| - Accuracy: 0.9129 | |
| ## 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: 5e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 64 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.366 | 1.0 | 938 | 0.3591 | 0.8902 | | |
| | 0.3217 | 2.0 | 1876 | 0.2900 | 0.9049 | | |
| | 0.2598 | 3.0 | 2814 | 0.2617 | 0.9129 | | |
| ### Framework versions | |
| - Transformers 4.52.4 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.2 | |