Image Classification
Transformers
TensorBoard
Safetensors
vit
seizure-detection
Generated from Trainer
Instructions to use JLB-JLB/seizure_vit_jlb_231112_fft_raw_combo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JLB-JLB/seizure_vit_jlb_231112_fft_raw_combo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JLB-JLB/seizure_vit_jlb_231112_fft_raw_combo") 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("JLB-JLB/seizure_vit_jlb_231112_fft_raw_combo") model = AutoModelForImageClassification.from_pretrained("JLB-JLB/seizure_vit_jlb_231112_fft_raw_combo") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_loss": 0.4821653366088867, | |
| "eval_roc_auc": 0.7666592334506762, | |
| "eval_runtime": 520.8761, | |
| "eval_samples_per_second": 60.252, | |
| "eval_steps_per_second": 7.532, | |
| "total_flos": 2.165002202648317e+19, | |
| "train_loss": 0.34656610874380017, | |
| "train_runtime": 7351.286, | |
| "train_samples_per_second": 38.005, | |
| "train_steps_per_second": 1.188 | |
| } |