Instructions to use CVPROJ25/FINETUNED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CVPROJ25/FINETUNED with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="CVPROJ25/FINETUNED") 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("CVPROJ25/FINETUNED") model = AutoModelForImageClassification.from_pretrained("CVPROJ25/FINETUNED", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_global_step": 32, | |
| "best_metric": 1.1013036966323853, | |
| "best_model_checkpoint": "./finetuning/checkpoint-32", | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 48, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.625, | |
| "grad_norm": 2.963273286819458, | |
| "learning_rate": 4.71875e-05, | |
| "loss": 1.1393531799316405, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.7275, | |
| "eval_loss": 1.117279052734375, | |
| "eval_runtime": 99.7594, | |
| "eval_samples_per_second": 20.048, | |
| "eval_steps_per_second": 1.253, | |
| "step": 16 | |
| }, | |
| { | |
| "epoch": 1.25, | |
| "grad_norm": 1.9508087635040283, | |
| "learning_rate": 4.40625e-05, | |
| "loss": 0.8301602363586426, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 1.875, | |
| "grad_norm": 1.7418757677078247, | |
| "learning_rate": 4.09375e-05, | |
| "loss": 0.5593328952789307, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.733, | |
| "eval_loss": 1.1013036966323853, | |
| "eval_runtime": 99.7792, | |
| "eval_samples_per_second": 20.044, | |
| "eval_steps_per_second": 1.253, | |
| "step": 32 | |
| }, | |
| { | |
| "epoch": 2.5, | |
| "grad_norm": 1.2206847667694092, | |
| "learning_rate": 3.78125e-05, | |
| "loss": 0.4049041271209717, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.729, | |
| "eval_loss": 1.130679965019226, | |
| "eval_runtime": 98.8399, | |
| "eval_samples_per_second": 20.235, | |
| "eval_steps_per_second": 1.265, | |
| "step": 48 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 160, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 10, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "EarlyStoppingCallback": { | |
| "args": { | |
| "early_stopping_patience": 4, | |
| "early_stopping_threshold": 0.0 | |
| }, | |
| "attributes": { | |
| "early_stopping_patience_counter": 1 | |
| } | |
| }, | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 1.876443281620992e+18, | |
| "train_batch_size": 512, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |