讯享网
https://juejin.cn/post/
cifar-10-/
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|– train/
| |– airplane/
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| |– automobile/
| | |– 1000_automobile.png
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| |– bird/
| | |– 10014_bird.png
| | |– 10015_bird.png
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|– test/
| |– airplane/
| | |– 10_airplane.png
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| |– automobile/
| | |– 100_automobile.png
| | |– 101_automobile.png
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| |– bird/
| | |– 1000_bird.png
| | |– 1001_bird.png
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|– val/ (optional)
| |– airplane/
| | |– 105_airplane.png
| | |– 106_airplane.png
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| |– automobile/
| | |– 102_automobile.png
| | |– 103_automobile.png
| | |– …
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| |– bird/
| | |– 1045_bird.png
| | |– 1046_bird.png
| | |– …
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| |– …
讯享网
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无法评估泛化能力：你不能验证模型在未见数据上的表现。 -
过拟合风险：可能无法检测到模型是否过拟合。
讯享网from datasets import load_dataset
ds = load_dataset(“amaye15/stanford-dogs”)
from ultralytics import YOLO
# Load a model
model = YOLO(“yolov8n-cls.pt”) # load a pretrained model (recommended for training)
# Train the model
results = model.train(data=“path/to/dataset”, epochs=100, imgsz=640)
讯享网# Ensure the model uses CPU
model.to(‘cpu’)
from ultralytics import YOLO
# Load a model
checkpoint = ‘your_model_path.pt’
model = YOLO(checkpoint) # pretrained YOLOv8n model
# Run batched inference on a list of images
results = model([“im1.jpg”, “im2.jpg”]) # return a list of Results objects
# Process results list
for result in results:
boxes = result.boxes # Boxes object for bounding box outputs
masks = result.masks # Masks object for segmentation masks outputs
keypoints = result.keypoints # Keypoints object for pose outputs
probs = result.probs # Probs object for classification outputs
obb = result.obb # Oriented boxes object for OBB outputs
result.show() # display to screen
result.save(filename=“result.jpg”) # save to disk
讯享网from ultralytics import YOLO
# Load a model
model = YOLO(“path/to/best.pt”) # 加载你自己训练数据集
# 导出为你需要的数据集
model.export(format=“onnx”)
model.train(
data=‘dataset’, # Path to your dataset configuration file
epochs=5, # Number of epochs
workers=4, # Number of workers
batch_size=16, # Batch size
augmented=True, # Use augmented data
)
model.export(format=‘onnx’,simplify=True)
https://github.com/jelychow/Android_Yolo8_Dog_Classfication
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