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| from typing import Optional | |
| from pydantic import Field | |
| from autotrain.trainers.common import AutoTrainParams | |
| class ImageClassificationParams(AutoTrainParams): | |
| """ | |
| ImageClassificationParams is a configuration class for image classification training parameters. | |
| Attributes: | |
| data_path (str): Path to the dataset. | |
| model (str): Pre-trained model name or path. Default is "google/vit-base-patch16-224". | |
| username (Optional[str]): Hugging Face account username. | |
| lr (float): Learning rate for the optimizer. Default is 5e-5. | |
| epochs (int): Number of epochs for training. Default is 3. | |
| batch_size (int): Batch size for training. Default is 8. | |
| warmup_ratio (float): Warmup ratio for learning rate scheduler. Default is 0.1. | |
| gradient_accumulation (int): Number of gradient accumulation steps. Default is 1. | |
| optimizer (str): Optimizer type. Default is "adamw_torch". | |
| scheduler (str): Learning rate scheduler type. Default is "linear". | |
| weight_decay (float): Weight decay for the optimizer. Default is 0.0. | |
| max_grad_norm (float): Maximum gradient norm for clipping. Default is 1.0. | |
| seed (int): Random seed for reproducibility. Default is 42. | |
| train_split (str): Name of the training data split. Default is "train". | |
| valid_split (Optional[str]): Name of the validation data split. | |
| logging_steps (int): Number of steps between logging. Default is -1. | |
| project_name (str): Name of the project for output directory. Default is "project-name". | |
| auto_find_batch_size (bool): Automatically find optimal batch size. Default is False. | |
| mixed_precision (Optional[str]): Mixed precision training mode (fp16, bf16, or None). | |
| save_total_limit (int): Maximum number of checkpoints to keep. Default is 1. | |
| token (Optional[str]): Hugging Face Hub token for authentication. | |
| push_to_hub (bool): Whether to push the model to Hugging Face Hub. Default is False. | |
| eval_strategy (str): Evaluation strategy during training. Default is "epoch". | |
| image_column (str): Column name for images in the dataset. Default is "image". | |
| target_column (str): Column name for target labels in the dataset. Default is "target". | |
| log (str): Logging method for experiment tracking. Default is "none". | |
| early_stopping_patience (int): Number of epochs with no improvement for early stopping. Default is 5. | |
| early_stopping_threshold (float): Threshold for early stopping. Default is 0.01. | |
| """ | |
| data_path: str = Field(None, title="Path to the dataset") | |
| model: str = Field("google/vit-base-patch16-224", title="Pre-trained model name or path") | |
| username: Optional[str] = Field(None, title="Hugging Face account username") | |
| lr: float = Field(5e-5, title="Learning rate for the optimizer") | |
| epochs: int = Field(3, title="Number of epochs for training") | |
| batch_size: int = Field(8, title="Batch size for training") | |
| warmup_ratio: float = Field(0.1, title="Warmup ratio for learning rate scheduler") | |
| gradient_accumulation: int = Field(1, title="Number of gradient accumulation steps") | |
| optimizer: str = Field("adamw_torch", title="Optimizer type") | |
| scheduler: str = Field("linear", title="Learning rate scheduler type") | |
| weight_decay: float = Field(0.0, title="Weight decay for the optimizer") | |
| max_grad_norm: float = Field(1.0, title="Maximum gradient norm for clipping") | |
| seed: int = Field(42, title="Random seed for reproducibility") | |
| train_split: str = Field("train", title="Name of the training data split") | |
| valid_split: Optional[str] = Field(None, title="Name of the validation data split") | |
| logging_steps: int = Field(-1, title="Number of steps between logging") | |
| project_name: str = Field("project-name", title="Name of the project for output directory") | |
| auto_find_batch_size: bool = Field(False, title="Automatically find optimal batch size") | |
| mixed_precision: Optional[str] = Field(None, title="Mixed precision training mode (fp16, bf16, or None)") | |
| save_total_limit: int = Field(1, title="Maximum number of checkpoints to keep") | |
| token: Optional[str] = Field(None, title="Hugging Face Hub token for authentication") | |
| push_to_hub: bool = Field(False, title="Whether to push the model to Hugging Face Hub") | |
| eval_strategy: str = Field("epoch", title="Evaluation strategy during training") | |
| image_column: str = Field("image", title="Column name for images in the dataset") | |
| target_column: str = Field("target", title="Column name for target labels in the dataset") | |
| log: str = Field("none", title="Logging method for experiment tracking") | |
| early_stopping_patience: int = Field(5, title="Number of epochs with no improvement for early stopping") | |
| early_stopping_threshold: float = Field(0.01, title="Threshold for early stopping") | |