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
metadata
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: []
Blood-Cell
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:
- 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