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ultralytics 8.0.181 RTDETR, MLFlow fixes and Examples updates (#4927)

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Co-authored-by: 唐洁 <tangjie1953479@tongji.edu.cn>
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24
docs/guides/kfold-cross-validation.md
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@ -83,6 +83,7 @@ Without further ado, let's dive in!
3. Now, read the contents of the dataset YAML file and extract the indices of the class labels.
```python
yaml_file = 'path/to/data.yaml' # your data YAML with data directories and names dictionary
with open(yaml_file, 'r', encoding="utf8") as y:
classes = yaml.safe_load(y)['names']
cls_idx = sorted(classes.keys())
@ -177,10 +178,18 @@ The ideal scenario is for all class ratios to be reasonably similar for each spl
4. Next, we create the directories and dataset YAML files for each split.
```python
supported_extensions = ['.jpg', '.jpeg', '.png']
# Initialize an empty list to store image file paths
images = []
# Loop through supported extensions and gather image files
for ext in supported_extensions:
images.extend(sorted((dataset_path / 'images').rglob(f"*{ext}")))
# Create the necessary directories and dataset YAML files (unchanged)
save_path = Path(dataset_path / f'{datetime.date.today().isoformat()}_{ksplit}-Fold_Cross-val')
save_path.mkdir(parents=True, exist_ok=True)
images = sorted((dataset_path / 'images').rglob("*.jpg")) # change file extension as needed
ds_yamls = []
for split in folds_df.columns:
@ -216,8 +225,7 @@ The ideal scenario is for all class ratios to be reasonably similar for each spl
img_to_path = save_path / split / k_split / 'images'
lbl_to_path = save_path / split / k_split / 'labels'
# Copy image and label files to new directory
# Might throw a SamefileError if file already exists
# Copy image and label files to new directory (SamefileError if file already exists)
shutil.copy(image, img_to_path / image.name)
shutil.copy(label, lbl_to_path / label.name)
```
@ -244,9 +252,15 @@ fold_lbl_distrb.to_csv(save_path / "kfold_label_distribution.csv")
```python
results = {}
# Define your additional arguments here
batch = 16
project = 'kfold_demo'
epochs = 100
for k in range(ksplit):
dataset_yaml = ds_yamls[k]
model.train(data=dataset_yaml, *args, **kwargs) # Include any training arguments
model.train(data=dataset_yaml,epochs=epochs, batch=batch, project=project) # include any train arguments
results[k] = model.metrics # save output metrics for further analysis
```