yolo-training
This skill should be used when user asks to "improve my mAP", "why is my model overfitting", "my training is diverging", "read my results.csv", "interpret my training curves", "my AP50 is good but AP50-95 is bad", "my recall is low", "how do I pick learning rate", "which augmentations should I use", "should I use a bigger model", "tune hyperparameters", or asks how to train YOLO26 for detection, instance or semantic segmentation, pose, OBB, classification, or depth.
npx skills add fcakyon/claude-codex-settings --skill yolo-training --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# YOLO26 training Read the run before changing anything. The `results.csv` and confusion matrix usually name the problem already. ## Order of operations Ordered by cost to try, cheapest first, not by size of the potential win. 1. **Epochs and schedule.** Undertrained looks like every other problem, and it costs nothing but time to rule out. 2. **Augmentation.** The knob for the generalization gap, at no extra compute per epoch. 3. **Loss weights and LR.** Cheap, and the curves usually say which one is wrong. 4. **Model size.** Scale up when train loss is still falling at the end of the schedule and the train and val curves sit close together. That is underfitting, and it is the only case a bigger model reliably fixes. 5. **Resolution.** Compute scales with the square of `imgsz`, so 640 to 1280 is roughly 4x the training budget, and pretrained weights transfer worse the further you move from the size they were fit at. Justify it with the object sizes in your data, not as a default first move. 6. **Data**, label quality and class balance. The highest ceiling and the slowest to move. The package ships no dataset-analysis tooling, so any audit here is your own script plus looking at im
- Order of operations
- Diagnostic loop
- Defaults that will surprise you
- Starting recipe
yolo train model=yolo26s.pt data=my-data.yaml epochs=200 imgsz=640 batch=16 \
What does the yolo-training skill do?
This skill should be used when user asks to "improve my mAP", "why is my model overfitting", "my training is diverging", "read my results.csv", "interpret my training curves", "my AP50 is good but AP50-95 is bad", "my recall is low", "how do I pick learning rate", "which augmentations should I use", "should I use a bigger model", "tune hyperparameters", or asks how to train YOLO26 for detection, instance or semantic segmentation, pose, OBB, classification, or depth.
How do I install it?
Run `npx skills add fcakyon/claude-codex-settings --skill yolo-training --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From fcakyon/claude-codex-settings, a repository with 967 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.