Instructions to use tzhao3/vit-GTSRB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/vit-GTSRB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tzhao3/vit-GTSRB") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("tzhao3/vit-GTSRB") model = AutoModelForImageClassification.from_pretrained("tzhao3/vit-GTSRB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from tzhao3/vit-GTSRB: direct link, hf CLI and curl.
- Browser
- Download file 1.98 kB
-
https://huggingface.co/tzhao3/vit-GTSRB/resolve/main/trainer_state.json
- Command line
-
hf download hf://tzhao3/vit-GTSRB/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/tzhao3/vit-GTSRB/resolve/main/trainer_state.json
1.98 kB
| { | |
| "best_metric": 0.956081081081081, | |
| "best_model_checkpoint": "fine-tune-vit-gtsrb\\checkpoint-1665", | |
| "epoch": 5.0, | |
| "global_step": 1665, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.9117867867867868, | |
| "eval_loss": 0.35636866092681885, | |
| "eval_runtime": 62.2454, | |
| "eval_samples_per_second": 85.597, | |
| "eval_steps_per_second": 1.349, | |
| "step": 333 | |
| }, | |
| { | |
| "epoch": 1.5, | |
| "learning_rate": 0.00016996996996997, | |
| "loss": 0.7992, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.9221096096096096, | |
| "eval_loss": 0.26261237263679504, | |
| "eval_runtime": 61.242, | |
| "eval_samples_per_second": 86.999, | |
| "eval_steps_per_second": 1.372, | |
| "step": 666 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9290540540540541, | |
| "eval_loss": 0.24274781346321106, | |
| "eval_runtime": 28.1132, | |
| "eval_samples_per_second": 189.52, | |
| "eval_steps_per_second": 2.988, | |
| "step": 999 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "learning_rate": 0.00013993993993993996, | |
| "loss": 0.2613, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "eval_accuracy": 0.9275525525525525, | |
| "eval_loss": 0.23870107531547546, | |
| "eval_runtime": 61.9681, | |
| "eval_samples_per_second": 85.98, | |
| "eval_steps_per_second": 1.356, | |
| "step": 1332 | |
| }, | |
| { | |
| "epoch": 4.5, | |
| "learning_rate": 0.00010990990990990993, | |
| "loss": 0.1934, | |
| "step": 1500 | |
| }, | |
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.956081081081081, | |
| "eval_loss": 0.14925633370876312, | |
| "eval_runtime": 61.7828, | |
| "eval_samples_per_second": 86.238, | |
| "eval_steps_per_second": 1.36, | |
| "step": 1665 | |
| } | |
| ], | |
| "max_steps": 3330, | |
| "num_train_epochs": 10, | |
| "total_flos": 8.260580814172324e+18, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |