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Docs cleanup and Google-style tracker docstrings (#6751)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-12-03 04:12:33 +01:00 • committed by GitHub
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14
docs/en/usage/callbacks.md
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@ -15,15 +15,19 @@ Ultralytics framework supports callbacks as entry points in strategic stages of
In this example, we want to return the original frame with each result object. Here's how we can do that
```python
from ultralytics import YOLO
def on_predict_batch_end(predictor):
# Retrieve the batch data
_, im0s, _, _ = predictor.batch
_, image, _, _ = predictor.batch
# Ensure that im0s is a list
im0s = im0s if isinstance(im0s, list) else [im0s]
# Ensure that image is a list
image = image if isinstance(image, list) else [image]
# Combine the prediction results with the corresponding frames
predictor.results = zip(predictor.results, im0s)
predictor.results = zip(predictor.results, image)
# Create a YOLO model instance
model = YOLO(f'yolov8n.pt')
@ -32,7 +36,7 @@ model = YOLO(f'yolov8n.pt')
model.add_callback("on_predict_batch_end", on_predict_batch_end)
# Iterate through the results and frames
for (result, frame) in model.track/predict():
for (result, frame) in model.predict(): # or model.track()
pass
```

2
docs/en/usage/engine.md
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@ -66,7 +66,7 @@ class CustomTrainer(DetectionTrainer):
# callback to upload model weights
def log_model(trainer):
last_weight_path = trainer.last
...
print(last_weight_path)
trainer = CustomTrainer(overrides={...})