The number of steps and images required to generate a checkpoint in Tensor Art depends on several factors, including your model architecture, the complexity of the task, and the quality of the data. Here's a breakdown to help you estimate:
1. Number of Steps
The required number of steps depends on:
Dataset Size: Larger datasets need more steps for sufficient training.
Learning Rate and Convergence: Smaller learning rates typically require more steps for the model to converge.
Task Complexity: Complex tasks (e.g., image generation, multi-class classification) need more training steps than simpler tasks.
General Guidelines:
Small Dataset (e.g., 1,000 images): 1,000–5,000 steps.
Medium Dataset (e.g., 10,000–50,000 images): 10,000–50,000 steps.
Large Dataset (e.g., >100,000 images): 50,000+ steps, often with early stopping to prevent overfitting.
2. Number of Images
For generating a meaningful checkpoint:
The model typically needs at least 1,000–10,000 diverse images for tasks like image generation or classification.
For high-quality results, datasets like COCO (Common Objects in Context) or ImageNet often include 50,000+ images.
If you're working with custom data:
Aim for a minimum of 1,000 images for fine-tuning pre-trained models.
If training from scratch, 10,000–50,000 images is a good starting point for robust model performance.
3. When to Create Checkpoints
Checkpoints are typically saved during training:
After each epoch (one pass through the dataset).
At regular intervals (e.g., every 1,000 steps).
Based on validation performance, to save the best-performing model.
Example Workflow
If you have 10,000 images:
Set up training for 20,000 steps (2 epochs if batch size = 32).
Save checkpoints every 1,000 steps or at the end of each epoch.
Evaluate the model after each checkpoint to decide if further training is necessary.
Key Takeaway
Steps: 1,000–50,000+ depending on task and dataset size.
Images: 1,000+ (fine-tuning) or 10,000+ (training from scratch).
Checkpoints: Save at regular intervals to monitor progress and ensure you don't lose training data in case of interruptions.
