Instructions to use Larbz-7/with_pneumo_train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Larbz-7/with_pneumo_train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Larbz-7/with_pneumo_train") 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("Larbz-7/with_pneumo_train") model = AutoModelForImageClassification.from_pretrained("Larbz-7/with_pneumo_train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
radioup-model-pneumo
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5810
- Accuracy: 0.875
- F1: 0.9333
- Precision: 1.0
- Recall: 0.875
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 2.5132 | 1.0 | 485 | 2.7588 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3143 | 2.0 | 970 | 1.2699 | 0.75 | 0.8571 | 1.0 | 0.75 |
| 0.9619 | 3.0 | 1455 | 0.8034 | 0.75 | 0.8571 | 1.0 | 0.75 |
| 0.3304 | 4.0 | 1940 | 0.6284 | 0.75 | 0.8571 | 1.0 | 0.75 |
| 0.6791 | 5.0 | 2425 | 0.5810 | 0.875 | 0.9333 | 1.0 | 0.875 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
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