Instructions to use Docty/Blood-Cell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Docty/Blood-Cell with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Docty/Blood-Cell") 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("Docty/Blood-Cell") model = AutoModelForImageClassification.from_pretrained("Docty/Blood-Cell", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Docty/Blood-Cell: direct link, hf CLI and curl.
- Browser
- Download file 2.2 kB
-
https://huggingface.co/Docty/Blood-Cell/resolve/main/README.md
- Command line
-
hf download hf://Docty/Blood-Cell/README.md
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curl -L -o README.md https://huggingface.co/Docty/Blood-Cell/resolve/main/README.md
2.2 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google/vit-base-patch16-224-in21k | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: Blood-Cell | |
| results: [] | |
| <!-- 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. --> | |
| # Blood-Cell | |
| 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: | |
| - Train Loss: 1.9897 | |
| - Validation Loss: 1.9904 | |
| - Train Accuracy: 0.3905 | |
| - Epoch: 9 | |
| ## 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: | |
| - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 10, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 | | |
| |:----------:|:---------------:|:--------------:|:-----:| | |
| | 1.9904 | 1.9904 | 0.3905 | 0 | | |
| | 1.9894 | 1.9904 | 0.3905 | 1 | | |
| | 1.9898 | 1.9904 | 0.3905 | 2 | | |
| | 1.9897 | 1.9904 | 0.3905 | 3 | | |
| | 1.9897 | 1.9904 | 0.3905 | 4 | | |
| | 1.9901 | 1.9904 | 0.3905 | 5 | | |
| | 1.9897 | 1.9904 | 0.3905 | 6 | | |
| | 1.9897 | 1.9904 | 0.3905 | 7 | | |
| | 1.9902 | 1.9904 | 0.3905 | 8 | | |
| | 1.9897 | 1.9904 | 0.3905 | 9 | | |
| ### Framework versions | |
| - Transformers 4.46.2 | |
| - TensorFlow 2.17.1 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.3 | |