Instructions to use sgraham/met_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgraham/met_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="sgraham/met_model") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("sgraham/met_model") model = AutoModelForZeroShotImageClassification.from_pretrained("sgraham/met_model") - Notebooks
- Google Colab
- Kaggle
met images 30 epochs
Browse files{'loss': 0.4739, 'learning_rate': 2.0286396181384247e-05, 'epoch': 17.86}
{'train_runtime': 4074.7723, 'train_samples_per_second': 10.19, 'train_steps_per_second': 0.206, 'train_loss': 0.3362658455258324, 'epoch': 30.0}
100% 840/840 [1:07:54<00:00, 4.85s/it]
***** train metrics *****
epoch = 30.0
train_loss = 0.3363
train_runtime = 1:07:54.77
train_samples_per_second = 10.19
train_steps_per_second = 0.206
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pytorch_model.bin
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