Instructions to use prithivMLmods/Clipart-126-DomainNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Clipart-126-DomainNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Clipart-126-DomainNet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Clipart-126-DomainNet") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Clipart-126-DomainNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-1392/trainer_state.json from prithivMLmods/Clipart-126-DomainNet: direct link, hf CLI and curl.
- Browser
- Download file 2.04 kB
-
https://huggingface.co/prithivMLmods/Clipart-126-DomainNet/resolve/main/checkpoint-1392/trainer_state.json
- Command line
-
hf download hf://prithivMLmods/Clipart-126-DomainNet/checkpoint-1392/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/prithivMLmods/Clipart-126-DomainNet/resolve/main/checkpoint-1392/trainer_state.json
2.04 kB
| { | |
| "best_global_step": 1392, | |
| "best_metric": 0.7880541086196899, | |
| "best_model_checkpoint": "siglip2-finetune-full/checkpoint-1392", | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 1392, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.6359832635983264, | |
| "eval_loss": 2.0701308250427246, | |
| "eval_model_preparation_time": 0.0031, | |
| "eval_runtime": 215.6864, | |
| "eval_samples_per_second": 68.702, | |
| "eval_steps_per_second": 8.591, | |
| "step": 464 | |
| }, | |
| { | |
| "epoch": 1.0775862068965518, | |
| "grad_norm": 49.15087890625, | |
| "learning_rate": 2.4052863436123347e-06, | |
| "loss": 3.5107, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.8090160615467674, | |
| "eval_loss": 1.0770337581634521, | |
| "eval_model_preparation_time": 0.0031, | |
| "eval_runtime": 213.0035, | |
| "eval_samples_per_second": 69.567, | |
| "eval_steps_per_second": 8.699, | |
| "step": 928 | |
| }, | |
| { | |
| "epoch": 2.1551724137931036, | |
| "grad_norm": 57.83103561401367, | |
| "learning_rate": 1.7444933920704845e-06, | |
| "loss": 1.3776, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.8484275880685652, | |
| "eval_loss": 0.7880541086196899, | |
| "eval_model_preparation_time": 0.0031, | |
| "eval_runtime": 213.1605, | |
| "eval_samples_per_second": 69.516, | |
| "eval_steps_per_second": 8.693, | |
| "step": 1392 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 2320, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 5, | |
| "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": 3.7270953003136205e+18, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |