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Why does my object detection model miss small objects even though it detects large ones accurately?
This happens because most detection architectures naturally favor large objects due to how feature maps are constructed. In convolutional networks, deeper layers capture high-level features but at the cost of spatial resolution. Small objects can disappear in these layers, making them difficult forRead more
This happens because most detection architectures naturally favor large objects due to how feature maps are constructed. In convolutional networks, deeper layers capture high-level features but at the cost of spatial resolution. Small objects can disappear in these layers, making them difficult for the detector to recognize.
If your model uses only high-level feature maps for detection, the network simply does not see enough detail to identify small items. This is why modern detectors use feature pyramids or multi-scale feature maps. Without these, the network cannot learn reliable representations for objects that occupy only a few pixels.
Using architectures with feature pyramid networks (FPN), increasing input resolution, and adding more small-object examples to the training set all improve this behavior. You should also check anchor sizes and ensure they match the scale of objects in your dataset.
See lessWhy does my medical imaging model perform well on one hospital’s data but poorly on another’s?
This happens because the model learned scanner-specific patterns instead of disease features. Differences in equipment, resolution, contrast, and noise create hidden signatures that neural networks can easily latch onto. When the model sees data from a new hospital, those hidden cues disappear, so tRead more
This happens because the model learned scanner-specific patterns instead of disease features. Differences in equipment, resolution, contrast, and noise create hidden signatures that neural networks can easily latch onto.
When the model sees data from a new hospital, those hidden cues disappear, so the learned representations no longer match. This is a classic case of domain shift.
Training on multi-source data, using domain-invariant features, and applying normalization across imaging styles improves cross-hospital generalization.
Common mistakes:
Training on a single source
Ignoring domain variation
No normalization between datasets
The practical takeaway is that medical models must be trained across domains to generalize safely.
See lessWhy does my reinforcement learning agent behave unpredictably in real environments?
This happens because simulations never perfectly match reality. The model learns simulation-specific dynamics that do not transfer. This is known as the sim-to-real gap. Even tiny differences in friction, timing, or noise can break learned policies. Domain randomization and real-world fine-tuning heRead more
This happens because simulations never perfectly match reality. The model learns simulation-specific dynamics that do not transfer.
This is known as the sim-to-real gap. Even tiny differences in friction, timing, or noise can break learned policies.
Domain randomization and real-world fine-tuning help close this gap.
Common mistakes:
Overfitting to simulation
No noise injection
No real-world adaptation
The practical takeaway is that real environments require real data.
See lessWhy does my model train slower when I add more GPU memory?
This happens because increasing GPU memory usually leads people to increase batch size, and large batches change how neural networks learn. While each step processes more data, the model receives fewer gradient updates per epoch, which can slow down learning even if raw computation is faster. LargeRead more
This happens because increasing GPU memory usually leads people to increase batch size, and large batches change how neural networks learn. While each step processes more data, the model receives fewer gradient updates per epoch, which can slow down learning even if raw computation is faster.
Large batches tend to smooth out gradient noise, which reduces the regularizing effect that smaller batches naturally provide. This often causes the optimizer to take more conservative steps, requiring more epochs to reach the same level of performance. As a result, even though each batch runs faster, the model may need more total training time to converge.
To compensate, you usually need to scale the learning rate upward or use gradient accumulation strategies. Without these adjustments, more GPU memory simply changes the training dynamics instead of making the model better or faster.
Common mistakes:
Increasing batch size without adjusting learning rate
Assuming more VRAM always improves training
Ignoring convergence behavior
The practical takeaway is that GPU memory changes how learning happens, not just how much data you can fit.
See lessWhy does my multimodal model fail when one input is missing?
This happens because the model was never trained to handle missing modalities. During training, it learned to rely on both image and text features simultaneously, so removing one breaks the learned representations. Neural networks do not automatically know how to compensate for missing data. If everRead more
This happens because the model was never trained to handle missing modalities. During training, it learned to rely on both image and text features simultaneously, so removing one breaks the learned representations.
Neural networks do not automatically know how to compensate for missing data. If every training example contains all inputs, the model assumes they will always be present and builds internal dependencies around them.
To fix this, you must train the model with masked or dropped modalities so it learns to fall back on whatever information is available. This is standard practice in robust multimodal systems.
Common mistakes:
Training only on complete data
No modality dropout
Assuming fusion layers are adaptive
The practical takeaway is that multimodal robustness must be trained explicitly.
See lessWhy does my speech recognition model work well in quiet rooms but fail in noisy environments?
This happens because the model learned to associate clean audio patterns with words and was never exposed to noisy conditions during training. Neural networks assume that test data looks like training data, and when noise changes that distribution, predictions break down. If most training samples arRead more
This happens because the model learned to associate clean audio patterns with words and was never exposed to noisy conditions during training. Neural networks assume that test data looks like training data, and when noise changes that distribution, predictions break down.
If most training samples are clean, the model learns very fine-grained acoustic features that do not generalize well. In noisy environments, those features are masked, so the network cannot match what it learned.
The solution is to include noise augmentation during training, such as adding background sounds, reverberation, and random distortions. This teaches the model to focus on speech-relevant signals rather than fragile acoustic details.
Common mistakes: Training only on studio-quality recordings, no data augmentation for audio ,ignoring real-world noise patterns
The practical takeaway is that robustness must be trained explicitly using noisy examples.
See lessWhy does my recommendation model become worse after adding more user data?
This happens when the new data has a different distribution than the old data. If recent user behavior differs from historical patterns, the model starts optimizing for conflicting signals. Neural networks are sensitive to data distribution shifts. When you mix old and new behaviors without proper wRead more
This happens when the new data has a different distribution than the old data. If recent user behavior differs from historical patterns, the model starts optimizing for conflicting signals.
Neural networks are sensitive to data distribution shifts. When you mix old and new behaviors without proper weighting, the model may lose previously learned structure and produce worse recommendations.
Using time-aware sampling, recency weighting, or retraining with sliding windows helps the model adapt without destroying prior knowledge.
Common mistakes:
Mixing old and new data blindly
Not tracking data drift
Overwriting historical patterns
The practical takeaway is that more data only helps if it is consistent with what the model is learning.
See lessWhy does my generative model produce unrealistic faces?
This happens when the model fails to learn correct spatial relationships between facial features. If the training data or architecture is weak, the generator learns textures without structure. High-resolution faces require strong inductive biases such as convolutional layers, attention, or progressiRead more
This happens when the model fails to learn correct spatial relationships between facial features. If the training data or architecture is weak, the generator learns textures without structure.
High-resolution faces require strong inductive biases such as convolutional layers, attention, or progressive growing to maintain geometry.
Better architectures and higher-quality aligned training data significantly improve realism.
Common mistakes: Low-resolution training, Poor alignment, Weak generator
The practical takeaway is that realism requires learning both texture and structure.
See lessWhy does my AI system behave correctly in testing but fail under real user load?
This happens because real-world usage introduces input patterns, concurrency, and timing effects not present in testing. Models trained on static datasets may fail when exposed to live data streams. Serving systems also face numerical drift, caching issues, and resource contention, which affect predRead more
This happens because real-world usage introduces input patterns, concurrency, and timing effects not present in testing. Models trained on static datasets may fail when exposed to live data streams.
Serving systems also face numerical drift, caching issues, and resource contention, which affect prediction quality even if the model itself is unchanged.
Monitoring, data drift detection, and continuous retraining are necessary for stable real-world deployment. Common mistakes are No production monitoring, No retraining pipelineAssuming test data represents reality
The practical takeaway is that deployment is part of the learning system, not separate from it.
See less