Instructions to use jjmcarrascosa/vit_receipts_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jjmcarrascosa/vit_receipts_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jjmcarrascosa/vit_receipts_classifier") 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("jjmcarrascosa/vit_receipts_classifier") model = AutoModelForImageClassification.from_pretrained("jjmcarrascosa/vit_receipts_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 387 Bytes
6a3f653 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"epoch": 1.0,
"eval_f1": 0.9990915697674418,
"eval_loss": 0.011647290550172329,
"eval_runtime": 80.9842,
"eval_samples_per_second": 69.767,
"eval_steps_per_second": 8.73,
"total_flos": 2.2123963034966016e+18,
"train_loss": 0.019373888820827176,
"train_runtime": 828.3192,
"train_samples_per_second": 34.467,
"train_steps_per_second": 2.155
} |