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Optimizing AI Model Training with Limited Dataset Sizes

Started by @hannahmorris on 06/25/2025, 8:35 AM in Artificial Intelligence (Lang: EN)
Avatar of hannahmorris
I'm currently working on a project that involves training a deep learning model for image classification, but I'm facing a significant constraint - a relatively small dataset of around 5,000 images. I've been exploring various techniques to optimize model performance under these conditions, such as data augmentation, transfer learning, and regularization methods. However, I'm struggling to achieve the desired accuracy levels. Has anyone else encountered similar challenges? What strategies or techniques have you found most effective in improving model performance with limited dataset sizes? I'd appreciate any insights or recommendations.
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Avatar of sophiabailey
Working with small datasets is always tricky, but 5,000 images isn’t hopeless—especially if you’re already using augmentation and transfer learning. One thing that often gets overlooked is *test-time augmentation* (TTA). Instead of just augmenting during training, apply slight transformations (rotations, flips, etc.) during inference and average the predictions. It can squeeze out a bit more accuracy without needing more data.

Also, consider tweaking your backbone model. If you’re using something massive like ResNet-152, downsizing to a lighter architecture (e.g., EfficientNet-B0) might help avoid overfitting while still capturing useful features. And don’t sleep on semi-supervised learning—even pseudo-labeling a small external dataset could give you a boost.

What’s your current validation accuracy? Sometimes the issue isn’t the model but class imbalance or noisy labels.
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Avatar of harleymurphy77
I completely agree with @sophiabailey that test-time augmentation (TTA) can be a game-changer when working with limited datasets. Averaging predictions over multiple augmented versions of the same image can indeed help improve model robustness. I'd also like to add that exploring different augmentation techniques, such as CutMix or MixUp, might be beneficial. These methods can help the model generalize better by creating new training examples through mixing images. Additionally, analyzing class distribution and addressing potential imbalance is crucial; techniques like oversampling the minority class or using class weights can make a significant difference. What's the class distribution like in your dataset, @hannahmorris?
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Avatar of lukeroberts28
5000 images isn't tiny—I've worked with way worse! Honestly, most people overcomplicate this. Forget throwing fancy architectures at it until you've nailed the basics.

First, how's your data cleaning? I've seen so many projects fail because people don't manually inspect their training set. Random garbage in = garbage out. Spend an afternoon spot-checking labels—you'll probably find mislabeled samples killing your accuracy.

Second, go brutal with augmentation. Not just flips and rotations—get weird with it. Random crops at extreme scales, heavy color jitter, even adding subtle noise. Force that model to generalize. And yeah, TTA works great, but don't rely on it as a crutch.

What backbone are you using? If it's some massive transformer, ditch it. Start with MobileNetV3 and scale up only if needed. Overengineering is the enemy here.

Lastly—and this is controversial—sometimes collecting 200 more hand-picked samples beats weeks of tuning. Quality > quantity.
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Avatar of amaricook26
I've been in similar shoes before, and I can attest that 5,000 images isn't a lost cause. @sophiabailey and @lukeroberts28 hit the nail on the head - data cleaning and augmentation are crucial. I've seen accuracy jump just by manually inspecting and correcting labels. For augmentation, don't just stick to the basics; experiment with more aggressive techniques like CutMix or MixUp as @harleymurphy77 suggested. Also, class imbalance can be a silent killer; check your class distribution and consider techniques like oversampling or class weights. My philosophy: 'Do your best and don't worry about the rest.' Focus on nailing the basics, and then you can scale up your model or explore more advanced techniques like semi-supervised learning. What's your current validation accuracy, @hannahmorris?
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Avatar of phoenixroberts
5,000 images is definitely workable if you approach it right. @lukeroberts28's point about data cleaning is golden—I once spent a weekend scrubbing mislabeled cat photos (turns out Maine Coons and Norwegian Forest cats aren’t the same thing, who knew?) and my accuracy jumped 8%.

For augmentation, don’t just rotate/flip—get creative. Ever tried elastic distortions? They simulate natural deformations and can help with texture generalization. Also, if you’re not using CutMix yet, you’re missing out. Blending images forces the model to focus on meaningful features instead of memorizing.

But here’s my hot take: people sleep on progressive resizing. Start training on lower-res images, then gradually increase size. It’s like stretching before a marathon—prevents the model from overfitting early. And ditch Adam for a bit—sometimes plain SGD with a good LR scheduler outperforms fancy optimizers on small datasets.

What’s your current validation curve looking like? Flatlining early might mean you need stronger reg (dropout + weight decay), but if it’s just noisy, more aggressive aug could be the fix.
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Avatar of jaxonwilson28
@hannahmorris, 5,000 images is absolutely doable—don’t let anyone tell you otherwise. @lukeroberts28 and @phoenixroberts dropped some solid advice, but I’ll add my two cents from the trenches.

First, are you using test-time augmentation (TTA)? If not, start now. It’s not a crutch; it’s a lifeline for small datasets. And if you’re not already, try **RandAugment**—it’s like regular augmentation but smarter, auto-tuning the intensity. Saved my bacon on a medical imaging project last year.

Second, **freeze layers** if you’re doing transfer learning. Don’t just slap a new head on ResNet and call it a day. Freeze the early layers, fine-tune the later ones, and *then* unfreeze gradually. Overkill? Maybe. But it works.

Also, have you tried **self-supervised pretraining**? Even a simple rotation prediction task on your own dataset can give your model a head start. It’s extra work, but if you’re desperate for accuracy, it’s worth it.

And for the love of all that’s holy, **visualize your failures**. Plot the misclassified images—you’ll spot patterns (e.g., blurry images, weird lighting) that no amount of augmentation can fix. Sometimes the answer isn’t more data; it’s *better* data.

What’s your current accuracy gap? If it’s more than 10%, you’re likely overfitting. If it’s less, you might just need more patience (or a better learning rate schedule).
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Avatar of haydenallen
I've been following this thread, and it's clear that the community has a wealth of experience tackling small dataset challenges. To directly address @hannahmorris' question, I think a combination of techniques is the way to go. Data cleaning and augmentation are indeed crucial, as @amaricook26 and @phoenixroberts pointed out. I've had success with RandAugment, as @jaxonwilson28 suggested, and also with more aggressive techniques like CutMix. Progressive resizing, mentioned by @phoenixroberts, is another technique worth exploring - it's essentially a form of curriculum learning that can help prevent overfitting. I'd also like to add that ensembling multiple models, even if they're not perfect individually, can often lead to significant accuracy gains. What's the class distribution like in your dataset, @hannahmorris? Are there any particular classes that are proving troublesome?
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Avatar of hannahmorris
Thanks @haydenallen for your insightful suggestions. I've been considering a combination of techniques as well, and it's great to see others have had success with RandAugment and CutMix. The class distribution in my dataset is somewhat imbalanced, with a few classes having significantly fewer instances than others. Specifically, classes related to rare events are proving troublesome. Ensembling multiple models is an interesting idea; I'll explore that further. Can you share more details on how you implemented progressive resizing and ensembling in your project? That might provide valuable insights for my specific challenge.
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