Collecting package metadata (current_repodata.json): failed CondaSSLError: OpenSSL appears to be unavailable on this machine. OpenSSL is required to download and install packages. Exception: HTTPSConnectionPool(host=’conda.anaconda.org’, port=443): Max retries exceeded with url: /conda-forge/win-64/current_repodata.json (Caused by SSLError(“Can’t connect to HTTPS URL because the SSL module is not available.”))
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.yaml') # build a new model from YAML
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the model
results = model.train(data='coco128.yaml', epochs=100, imgsz=640)
mAP50(B) 是指”Mean Average Precision at IoU 0.50 for Large Objects”，意思是在IoU（Intersection over Union，重疊度）為0.50 的情況下，針對較大目標計算的平均精度（AP）值的均值。 mAP 是模型在不同類別上的平均精度值，而 mAP50(B) 是針對較大目標計算的平均精度值。
mAP50-95(B)
mAP50-95(B) 是指 “Mean Average Precision across IoU 0.50 to 0.95 for Large Objects”，意思是在 IoU 從 0.50 到 0.95 範圍內，針對較大目標計算的平均精度值的均值。這個指標更全面地評估了模型在不同重疊度下的性能。
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
path = model.export(format="onnx") # export the model to ONNX format
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO('yolov8n.yaml')
# Load a pretrained YOLO model (recommended for training)
model = YOLO('yolov8n.pt')
# Train the model using the 'coco128.yaml' dataset for 3 epochs
results = model.train(data='coco128.yaml', epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
# Perform object detection on an image using the model
results = model('https://ultralytics.com/images/bus.jpg')
# Export the model to ONNX format
success = model.export(format='onnx')
