Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use Devarshi/Brain_Tumor_Classification_using_swin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devarshi/Brain_Tumor_Classification_using_swin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Devarshi/Brain_Tumor_Classification_using_swin") 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("Devarshi/Brain_Tumor_Classification_using_swin") model = AutoModelForImageClassification.from_pretrained("Devarshi/Brain_Tumor_Classification_using_swin") - Notebooks
- Google Colab
- Kaggle
File size: 320 Bytes
bdbd32e | 1 2 3 4 5 6 7 8 9 10 11 | {
"epoch": 3.0,
"eval_accuracy": 0.9960906958561376,
"eval_f1": 0.9960906958561376,
"eval_loss": 0.012254622764885426,
"eval_precision": 0.9960906958561376,
"eval_recall": 0.9960906958561376,
"eval_runtime": 436.7862,
"eval_samples_per_second": 5.856,
"eval_steps_per_second": 0.183
} |