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ultralytics 8.2.2 replace COCO128 with COCO8 (#10167)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-04-18 20:47:21 -07:00 • committed by GitHub
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docs/en/integrations/clearml.md
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@ -80,7 +80,7 @@ Before diving into the usage instructions, be sure to check out the range of [YO
model = YOLO(f'{model_variant}.pt')
# Step 4: Setting Up Training Arguments
args = dict(data="coco128.yaml", epochs=16)
args = dict(data="coco8.yaml", epochs=16)
task.connect(args)
# Step 5: Initiating Model Training
@ -97,7 +97,7 @@ Let’s understand the steps showcased in the usage code snippet above.
**Step 3: Loading the YOLOv8 Model**: The selected YOLOv8 model is loaded using Ultralytics' YOLO class, preparing it for training.
**Step 4: Setting Up Training Arguments**: Key training arguments like the dataset (`coco128.yaml`) and the number of epochs (`16`) are organized in a dictionary and connected to the ClearML task. This allows for tracking and potential modification via the ClearML UI. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
**Step 4: Setting Up Training Arguments**: Key training arguments like the dataset (`coco8.yaml`) and the number of epochs (`16`) are organized in a dictionary and connected to the ClearML task. This allows for tracking and potential modification via the ClearML UI. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
**Step 5: Initiating Model Training**: The model training is started with the specified arguments. The results of the training process are captured in the `results` variable.