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👋 Hello @bee1409, thank you for your interest in Ultralytics 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered. Thanks for sharing the detailed class stats and metrics 🙌—this kind of “minority class wins / majority class struggles” pattern can be tricky to interpret, and to help the team give you the most accurate guidance we’ll need a bit more context about your setup and data split 🔎 If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results. Please also include (as text) the exact command you used (CLI or Python), your Join the Ultralytics community where it suits you best. For real-time chat, head to Discord 🎧. Prefer in-depth discussions? Check out Discourse. Or dive into threads on our Subreddit to share knowledge with the community. UpgradeUpgrade to the latest pip install -U ultralyticsEnvironmentsYOLO may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
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If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLO Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit. This is an automated response 🤖—an Ultralytics engineer will also assist soon. |
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This pattern can be totally normal: per-class mAP is driven more by class separability, label consistency, and how “tight” the class definition is than by raw instance count, and with only 33 |
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Thanks Glenn, really appreciate the detailed guidance. I followed your suggestion and inspected the PR curve, raw/normalized confusion matrices, and validation batch predictions. For scars, the dominant failure mode is clearly false negatives to background, not confusion with other defect classes. Cross-class confusion is minimal, while ~35–40% of true scars are missed entirely, which aligns with the class’s high intra-class variability and low visual contrast. Based on this, I agree this is primarily a data/definition and visibility issue, rather than something loss weighting would fix. I’m planning to evaluate image tiling (e.g., 512–640 px tiles with overlap) since scars are thin and subtle and may benefit from increased effective resolution and reduced background context. If you have any rules-of-thumb on when tiling tends to help vs. hurt for YOLO-style detectors, I’d be very interested to hear your perspective. |
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Tiling usually helps YOLO-style detectors when the target signal is getting “washed out” by resizing (i.e., the defect becomes only a few pixels wide at your chosen |
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Thanks again for the clarification, this has been very helpful. I’m trying to anchor it to prior work. My understanding is that this pattern (majority class underperforming due to high intra-class variability and low separability, while rare but visually distinctive classes perform well) is fairly common in defect detection and other real-world detection tasks. Are there any papers or survey-style references you’d recommend that discuss this phenomenon (even broadly, e.g., in defect detection or imbalanced detection contexts)? I’m not looking for an exact match, just something that frames this behavior in the literature. |
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I'm seeing a counter-intuitive pattern where the rarest class in my dataset significantly outperforms the most common class. Wondering if this is expected YOLOv11 behavior or if I should adjust my approach.
Dataset Characteristics
Task: Multi-class object detection (rail defects)
Classes: 5
Extreme imbalance: 108:1 ratio
Class Instances
breaks 235 ← Rarest
cracks 1,915
lightband 1,915
rails 2,063
scars 24,546 ← Most common
model: yolov11s.pt
data: data.yaml
epochs: 100
imgsz: 1280
batch: 16
device: 0
All other params: default
Class Instances Precision Recall mAP50 mAP50-95
breaks 33 0.939 1.000 0.992 0.705
cracks 127 0.943 0.976 0.986 0.710
lightband 195 0.983 0.954 0.981 0.919
rails 200 0.961 0.975 0.984 0.947
scars 2,385 0.779 0.479 0.653 0.302
all 0.921 0.877 0.919 0.717
The Pattern
Unexpected observation:
Rarest class (breaks): Nearly perfect (0.992 mAP, 1.0 recall)
Most common class (scars): Worst performance (0.653 mAP, 0.479 recall)
This contradicts typical imbalance behavior where minority classes struggle.
Visual Characteristics:
Sharp edges, clear discontinuities
Visually very distinctive
-Scars (performing poorly):
High intra-class variance (size, shape, appearance)
Is this expected YOLOv11 behavior?
Does YOLOv11's architecture/loss function handle well-separated minority classes better than previous versions?
Should I apply balancing strategies?
Given that 4/5 classes are already near-perfect, would typical balancing (oversample/undersample) help or hurt?
Any YOLOv11-specific recommendations?
Are there config tweaks that might specifically help the scars class without affecting others?
Adjust cls loss weight?
Use focal loss (if available)?
Different augmentation strategy?
Have others seen this pattern?
Is this a known behavior with highly imbalanced but separable data?
Context
This is for research/thesis work. Overall 91.9% mAP is quite good, but I want to understand:
Whether this is fundamentally sound
If I should focus on improving scars specifically vs. global rebalancing
Whether this tells us something about YOLOv11's behavior with imbalanced data
Additional Info
Using imgsz=1280 (not 640) due to small/subtle defects
Test set held out, these are validation results
Any insights appreciated! Especially from folks who've trained YOLOv11 on similarly imbalanced datasets.
Thanks!
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