{
  "id": 505942,
  "title": "Increasing Epochs Based on Augmentations",
  "url": "/competitions/birdclef-2024/discussion/505942",
  "author_name": "",
  "post_date": "2024-05-19T19:31:11.543401Z",
  "votes": 2,
  "comment_count": 1,
  "views": 0,
  "content": "<p>This is probably a noob question, but is there a certain amount you increase the total epochs by for each additional augmentation?</p>",
  "messages": [
    {
      "id": "2824480",
      "postDate": "05/19/2024 19:31:11",
      "content": "<p>This is probably a noob question, but is there a certain amount you increase the total epochs by for each additional augmentation?</p>",
      "rawMarkdown": "This is probably a noob question, but is there a certain amount you increase the total epochs by for each additional augmentation?",
      "votes": null
    },
    {
      "id": "2824528",
      "postDate": "05/19/2024 20:21:17",
      "content": "<p>To make this easy to understand, let's suppose your dataset has 50 images. If you do no augmentation and choose a batch size of 10, then you will need a number of epochs higher than 5 (since 5 x 10 = 50) in order to pass all the images through the model.</p>\n<p>When you use augmentation techniques, the more you use, the bigger your training data will become. In this case, you will indeed need to increase the number of epochs in order to pass the maximum number of augmented data through the model. However, it is hard to predict how many epochs you will need.</p>\n<p>For this reason, it is important to test your model and see how the learning rate is progressing. Focus on other metrics first, such as how fast the model is learning, training accuracy, and test accuracy. Based on those metrics, you will have a clear vision of how many epochs are enough and when your accuracy will hit a limit.</p>\n<p>Don't forget to always save the model with the best accuracy achieved during training. This way, even if your estimation of epochs is wrong, your model is already saved.</p>\n<p>You can also use an early stopping mechanism where the training stops automatically when the learning rate converges near 0</p>",
      "rawMarkdown": "To make this easy to understand, let's suppose your dataset has 50 images. If you do no augmentation and choose a batch size of 10, then you will need a number of epochs higher than 5 (since 5 x 10 = 50) in order to pass all the images through the model.\n\nWhen you use augmentation techniques, the more you use, the bigger your training data will become. In this case, you will indeed need to increase the number of epochs in order to pass the maximum number of augmented data through the model. However, it is hard to predict how many epochs you will need.\n\nFor this reason, it is important to test your model and see how the learning rate is progressing. Focus on other metrics first, such as how fast the model is learning, training accuracy, and test accuracy. Based on those metrics, you will have a clear vision of how many epochs are enough and when your accuracy will hit a limit.\n\nDon't forget to always save the model with the best accuracy achieved during training. This way, even if your estimation of epochs is wrong, your model is already saved.\n\nYou can also use an early stopping mechanism where the training stops automatically when the learning rate converges near 0",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2824528,
      "author_name": "rabieelkharoua",
      "author_url": "",
      "post_date": "05/19/2024 20:21:17",
      "content": "<p>To make this easy to understand, let's suppose your dataset has 50 images. If you do no augmentation and choose a batch size of 10, then you will need a number of epochs higher than 5 (since 5 x 10 = 50) in order to pass all the images through the model.</p>\n<p>When you use augmentation techniques, the more you use, the bigger your training data will become. In this case, you will indeed need to increase the number of epochs in order to pass the maximum number of augmented data through the model. However, it is hard to predict how many epochs you will need.</p>\n<p>For this reason, it is important to test your model and see how the learning rate is progressing. Focus on other metrics first, such as how fast the model is learning, training accuracy, and test accuracy. Based on those metrics, you will have a clear vision of how many epochs are enough and when your accuracy will hit a limit.</p>\n<p>Don't forget to always save the model with the best accuracy achieved during training. This way, even if your estimation of epochs is wrong, your model is already saved.</p>\n<p>You can also use an early stopping mechanism where the training stops automatically when the learning rate converges near 0</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2824480": "This is probably a noob question, but is there a certain amount you increase the total epochs by for each additional augmentation?",
    "2824528": "To make this easy to understand, let's suppose your dataset has 50 images. If you do no augmentation and choose a batch size of 10, then you will need a number of epochs higher than 5 (since 5 x 10 = 50) in order to pass all the images through the model.\n\nWhen you use augmentation techniques, the more you use, the bigger your training data will become. In this case, you will indeed need to increase the number of epochs in order to pass the maximum number of augmented data through the model. However, it is hard to predict how many epochs you will need.\n\nFor this reason, it is important to test your model and see how the learning rate is progressing. Focus on other metrics first, such as how fast the model is learning, training accuracy, and test accuracy. Based on those metrics, you will have a clear vision of how many epochs are enough and when your accuracy will hit a limit.\n\nDon't forget to always save the model with the best accuracy achieved during training. This way, even if your estimation of epochs is wrong, your model is already saved.\n\nYou can also use an early stopping mechanism where the training stops automatically when the learning rate converges near 0"
  },
  "source": "meta"
}