Instructions to use DHEIVER/Brain_Tumor_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DHEIVER/Brain_Tumor_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DHEIVER/Brain_Tumor_Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("DHEIVER/Brain_Tumor_Classification") model = AutoModelForImageClassification.from_pretrained("DHEIVER/Brain_Tumor_Classification") - Notebooks
- Google Colab
- Kaggle
File size: 506 Bytes
dc2b267 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"epoch": 4.99,
"eval_accuracy": 0.9646761984861227,
"eval_f1": 0.9646761984861227,
"eval_loss": 0.10117336362600327,
"eval_precision": 0.9646761984861227,
"eval_recall": 0.9646761984861227,
"eval_runtime": 60.8804,
"eval_samples_per_second": 19.53,
"eval_steps_per_second": 0.624,
"total_flos": 1.3278811358711808e+18,
"train_loss": 0.383831198531461,
"train_runtime": 7560.7227,
"train_samples_per_second": 7.075,
"train_steps_per_second": 0.055
} |