Instructions to use ProbeX/Model-J__SupViT__model_idx_0293 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__SupViT__model_idx_0293 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0293") 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("ProbeX/Model-J__SupViT__model_idx_0293") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0293") - Notebooks
- Google Colab
- Kaggle
File size: 1,635 Bytes
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{
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"num_input_tokens_seen": 0,
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"stateful_callbacks": {
"TrainerControl": {
"args": {
"should_epoch_stop": false,
"should_evaluate": false,
"should_log": false,
"should_save": true,
"should_training_stop": true
},
"attributes": {}
}
},
"total_flos": 3.29482641245184e+18,
"train_batch_size": 64,
"trial_name": null,
"trial_params": null
}
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