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Compare 2+ YOLO models side-by-side on the same dataset — mAP, per-class AP, speed, size.
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| name | compare-models |
| description | Compare 2+ YOLO models side-by-side on the same dataset — mAP, per-class AP, speed, size. |
Compare 2+ YOLO models on the same validation dataset. All models are evaluated with the same data and imgsz for a fair comparison.
.pt file paths (required)data.yaml (optional — falls back to yolo-project.yaml defaults.dataset)yolo-project.yaml defaults.imgsz, then 640)yolo-project.yaml:python -c "
import yaml, pathlib
cfg_path = None
for p in [pathlib.Path('yolo-project.yaml')] + list(pathlib.Path('.').resolve().parents):
candidate = p / 'yolo-project.yaml' if p.is_dir() else p
if candidate.exists():
cfg_path = candidate
break
if cfg_path:
cfg = yaml.safe_load(cfg_path.read_text())
defaults = cfg.get('defaults', {})
print(f\"dataset: {defaults.get('dataset', 'NOT SET')}\")
print(f\"imgsz: {defaults.get('imgsz', 640)}\")
else:
print('No yolo-project.yaml found')
"
If no dataset is provided and none is in the project config, stop and ask the user.
data.yaml file is at {dataset}/data.yaml. Verify it exists.For each model, run validation with identical settings:
python -c "
import json, os
from ultralytics import YOLO
model_path = '<MODEL_PATH>'
data_yaml = '<DATA_YAML>'
imgsz = <IMGSZ>
device = 'cuda' if __import__('torch').cuda.is_available() else 'cpu'
model = YOLO(model_path)
results = model.val(data=data_yaml, imgsz=imgsz, device=device, verbose=False)
# File size in MB
size_mb = os.path.getsize(model_path) / (1024 * 1024)
# Inference speed (ms per image, preprocess + inference + postprocess)
speed = results.speed
total_ms = speed.get('preprocess', 0) + speed.get('inference', 0) + speed.get('postprocess', 0)
# Per-class AP50 values
class_names = results.names
per_class_ap50 = {}
per_class_ap50_95 = {}
if results.box.ap_class_index is not None:
for i, cls_idx in enumerate(results.box.ap_class_index):
name = class_names[int(cls_idx)]
per_class_ap50[name] = round(float(results.box.ap50[i]), 4)
per_class_ap50_95[name] = round(float(results.box.ap[i]), 4)
out = {
'model': model_path,
'mAP50': round(float(results.box.map50), 4),
'mAP50-95': round(float(results.box.map), 4),
'precision': round(float(results.box.mp), 4),
'recall': round(float(results.box.mr), 4),
'speed_ms': round(total_ms, 1),
'size_mb': round(size_mb, 1),
'per_class_ap50': per_class_ap50,
'per_class_ap50_95': per_class_ap50_95,
}
print(json.dumps(out))
"
Collect the JSON output from each run.
Build a markdown table from the collected results:
| Model | mAP50 | mAP50-95 | Precision | Recall | Speed (ms) | Size (MB) |
|-------|-------|----------|-----------|--------|------------|-----------|
| ... | ... | ... | ... | ... | ... | ... |
Sort by mAP50-95 descending. Bold the best value in each column.
Show per-class AP50 for each model. If exactly 2 models, add a delta column. If >2 models, show raw values and bold the best per class.
| Class | Model A | Model B | Delta |
|-------|---------|---------|-------|
| cat | 0.92 | 0.88 | +0.04 |
| dog | 0.85 | 0.91 | -0.06 |
State plainly:
ultralytics and torch are already available.Set up autonomous training monitoring — creates cron jobs to track long-running training, auto-continue pipeline when training completes.
Audit YOLO dataset quality — class distribution, annotation quality, image stats, and improvement suggestions.
Orchestrate the full active learning loop: train, analyze, push to CVAT, wait for review, pull, merge, retrain.
Analyze YOLO training runs — compares to baseline/best, checks per-class regression, analyzes training dynamics and tune convergence, writes actionable recommendations.
Run autonomous YOLO training experiments — reads training-plan.md, assesses bottlenecks, acts strategically, and delegates HP optimization to model.tune().
Profile YOLO model inference speed, FPS, and size across image sizes and export formats.