Instructions to use prithivMLmods/Fashion-Mnist-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Fashion-Mnist-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Fashion-Mnist-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/Fashion-Mnist-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Fashion-Mnist-SigLIP2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_global_step": 3750, | |
| "best_metric": 0.31374436616897583, | |
| "best_model_checkpoint": "Fashion-Mnist-SigLIP2/checkpoint-3750", | |
| "epoch": 2.0, | |
| "eval_steps": 500, | |
| "global_step": 3750, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.26666666666666666, | |
| "grad_norm": 9.494325637817383, | |
| "learning_rate": 0.00018385650224215247, | |
| "loss": 0.9674, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.5333333333333333, | |
| "grad_norm": 6.167793273925781, | |
| "learning_rate": 0.00016591928251121076, | |
| "loss": 0.6265, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 0.8, | |
| "grad_norm": 5.353821754455566, | |
| "learning_rate": 0.00014798206278026908, | |
| "loss": 0.5464, | |
| "step": 1500 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.8283, | |
| "eval_loss": 0.46604594588279724, | |
| "eval_model_preparation_time": 0.003, | |
| "eval_runtime": 687.6086, | |
| "eval_samples_per_second": 87.259, | |
| "eval_steps_per_second": 10.907, | |
| "step": 1875 | |
| }, | |
| { | |
| "epoch": 1.0666666666666667, | |
| "grad_norm": 3.131957530975342, | |
| "learning_rate": 0.00013004484304932735, | |
| "loss": 0.4901, | |
| "step": 2000 | |
| }, | |
| { | |
| "epoch": 1.3333333333333333, | |
| "grad_norm": 3.037127733230591, | |
| "learning_rate": 0.00011210762331838565, | |
| "loss": 0.4272, | |
| "step": 2500 | |
| }, | |
| { | |
| "epoch": 1.6, | |
| "grad_norm": 2.186342716217041, | |
| "learning_rate": 9.417040358744396e-05, | |
| "loss": 0.3883, | |
| "step": 3000 | |
| }, | |
| { | |
| "epoch": 1.8666666666666667, | |
| "grad_norm": 5.017179489135742, | |
| "learning_rate": 7.623318385650225e-05, | |
| "loss": 0.3628, | |
| "step": 3500 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.8862833333333333, | |
| "eval_loss": 0.31374436616897583, | |
| "eval_model_preparation_time": 0.003, | |
| "eval_runtime": 680.7322, | |
| "eval_samples_per_second": 88.14, | |
| "eval_steps_per_second": 11.018, | |
| "step": 3750 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 5625, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 1.005132625330176e+19, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |