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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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4
ultralytics/cfg/__init__.py
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@ -66,13 +66,13 @@ CLI_HELP_MSG = f"""
See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
2. Predict a YouTube video using a pretrained segmentation model at image size 320:
yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
3. Val a pretrained detection model at batch-size 1 and image size 640:
yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
yolo val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640
4. Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128

2
ultralytics/cfg/default.yaml
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@ -6,7 +6,7 @@ mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmar
# Train settings -------------------------------------------------------------------------------------------------------
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: # (str, optional) path to data file, i.e. coco128.yaml
data: # (str, optional) path to data file, i.e. coco8.yaml
epochs: 100 # (int) number of epochs to train for
time: # (float, optional) number of hours to train for, overrides epochs if supplied
patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training

4
ultralytics/cfg/models/README.md
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@ -11,7 +11,7 @@ To get started, simply browse through the models in this directory and find one
Model `*.yaml` files may be used directly in the Command Line Interface (CLI) with a `yolo` command:
```bash
yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
yolo task=detect mode=train model=yolov8n.yaml data=coco8.yaml epochs=100
```
They may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
@ -22,7 +22,7 @@ from ultralytics import YOLO
model = YOLO("model.yaml") # build a YOLOv8n model from scratch
# YOLO("model.pt") use pre-trained model if available
model.info() # display model information
model.train(data="coco128.yaml", epochs=100) # train the model
model.train(data="coco8.yaml", epochs=100) # train the model
```
## Pre-trained Model Architectures