Instructions to use prithivMLmods/Face-Confidence-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Face-Confidence-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Face-Confidence-SigLIP2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Face-Confidence-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Face-Confidence-SigLIP2") - Notebooks
- Google Colab
- Kaggle
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| "best_global_step": 2156, | |
| "best_metric": 0.3237510919570923, | |
| "best_model_checkpoint": "face-confidence/checkpoint-2156", | |
| "epoch": 4.0, | |
| "eval_steps": 500, | |
| "global_step": 2156, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
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| "epoch": 0.9276437847866419, | |
| "grad_norm": 6.720329761505127, | |
| "learning_rate": 1.5726495726495726e-05, | |
| "loss": 0.4994, | |
| "step": 500 | |
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| "epoch": 1.0, | |
| "eval_accuracy": 0.8189497518070191, | |
| "eval_loss": 0.3962201476097107, | |
| "eval_model_preparation_time": 0.0025, | |
| "eval_runtime": 141.0014, | |
| "eval_samples_per_second": 81.439, | |
| "eval_steps_per_second": 10.184, | |
| "step": 539 | |
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| { | |
| "epoch": 1.8552875695732838, | |
| "grad_norm": 8.098552703857422, | |
| "learning_rate": 1.0978157644824312e-05, | |
| "loss": 0.4083, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.8331446486109901, | |
| "eval_loss": 0.37225112318992615, | |
| "eval_model_preparation_time": 0.0025, | |
| "eval_runtime": 139.7716, | |
| "eval_samples_per_second": 82.155, | |
| "eval_steps_per_second": 10.274, | |
| "step": 1078 | |
| }, | |
| { | |
| "epoch": 2.782931354359926, | |
| "grad_norm": 13.027604103088379, | |
| "learning_rate": 6.229819563152897e-06, | |
| "loss": 0.3556, | |
| "step": 1500 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.8531742575981887, | |
| "eval_loss": 0.33761295676231384, | |
| "eval_model_preparation_time": 0.0025, | |
| "eval_runtime": 139.2575, | |
| "eval_samples_per_second": 82.459, | |
| "eval_steps_per_second": 10.312, | |
| "step": 1617 | |
| }, | |
| { | |
| "epoch": 3.7105751391465676, | |
| "grad_norm": 11.10226821899414, | |
| "learning_rate": 1.4814814814814815e-06, | |
| "loss": 0.3085, | |
| "step": 2000 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "eval_accuracy": 0.8599669076025429, | |
| "eval_loss": 0.3237510919570923, | |
| "eval_model_preparation_time": 0.0025, | |
| "eval_runtime": 139.6031, | |
| "eval_samples_per_second": 82.255, | |
| "eval_steps_per_second": 10.286, | |
| "step": 2156 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 2156, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 4, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 5.770418640105112e+18, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |