Instructions to use ProbeX/Model-J__SupViT__model_idx_0658 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_0658 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_0658") 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("ProbeX/Model-J__SupViT__model_idx_0658") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0658", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from ProbeX/Model-J__SupViT__model_idx_0658: direct link, hf CLI and curl.
- Browser
- Download file 2.04 kB
-
https://huggingface.co/ProbeX/Model-J__SupViT__model_idx_0658/resolve/main/trainer_state.json
- Command line
-
hf download hf://ProbeX/Model-J__SupViT__model_idx_0658/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/ProbeX/Model-J__SupViT__model_idx_0658/resolve/main/trainer_state.json
2.04 kB
| { | |
| "best_metric": 0.9397333333333333, | |
| "best_model_checkpoint": "./vit_finetuned_models_dataset/CIFAR100/50_from_100/google_vit-base-patch16-224/model_idx_0658/checkpoints/checkpoint-999", | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 999, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "grad_norm": 22.518024444580078, | |
| "learning_rate": 5e-05, | |
| "loss": 0.8955, | |
| "step": 333 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.9261333333333334, | |
| "eval_loss": 0.28051379323005676, | |
| "eval_runtime": 13.0216, | |
| "eval_samples_per_second": 287.983, | |
| "eval_steps_per_second": 4.531, | |
| "step": 333 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "grad_norm": 2.1316683292388916, | |
| "learning_rate": 5e-05, | |
| "loss": 0.1477, | |
| "step": 666 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.9365333333333333, | |
| "eval_loss": 0.22580453753471375, | |
| "eval_runtime": 13.0792, | |
| "eval_samples_per_second": 286.715, | |
| "eval_steps_per_second": 4.511, | |
| "step": 666 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "grad_norm": 0.11877024918794632, | |
| "learning_rate": 5e-05, | |
| "loss": 0.073, | |
| "step": 999 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9397333333333333, | |
| "eval_loss": 0.21564453840255737, | |
| "eval_runtime": 13.2128, | |
| "eval_samples_per_second": 283.817, | |
| "eval_steps_per_second": 4.465, | |
| "step": 999 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 999, | |
| "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": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 4.94223961867776e+18, | |
| "train_batch_size": 64, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |