PyTorch 허브

pip install -r https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Image
im = 'https://ultralytics.com/images/zidane.jpg'

# Inference
results = model(im)

results.pandas().xyxy[0]
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 1  433.50  433.50   517.5  714.5    0.687988     27     tie
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie

import cv2
import torch
from PIL import Image

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Images
for f in 'zidane.jpg', 'bus.jpg':
    torch.hub.download_url_to_file('https://ultralytics.com/images/' + f, f)  # download 2 images
im1 = Image.open('zidane.jpg')  # PIL image
im2 = cv2.imread('bus.jpg')[..., ::-1]  # OpenCV image (BGR to RGB)

# Inference
results = model([im1, im2], size=640)  # batch of images

# Results
results.print()
results.save()  # or .show()

results.xyxy[0]  # im1 predictions (tensor)
results.pandas().xyxy[0]  # im1 predictions (pandas)
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 1  433.50  433.50   517.5  714.5    0.687988     27     tie
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie

model.conf = 0.25  # NMS confidence threshold
iou = 0.45  # NMS IoU threshold
agnostic = False  # NMS class-agnostic
multi_label = False  # NMS multiple labels per box
classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
max_det = 1000  # maximum number of detections per image
amp = False  # Automatic Mixed Precision (AMP) inference

results = model(im, size=320)  # custom inference size

model.cpu()  # CPU
model.cuda()  # GPU
model.to(device)  # i.e. device=torch.device(0)

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', device='cpu')  # load on CPU

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', _verbose=False)  # load silently

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', channels=4)

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', classes=10)

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)  # force reload

import torch
from PIL import ImageGrab

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Image
im = ImageGrab.grab()  # take a screenshot

# Inference
results = model(im)

import torch
import threading


def run(model, im):
    results = model(im)
    results.save()


# Models
model0 = torch.hub.load('ultralytics/yolov5', 'yolov5s', device=0)
model1 = torch.hub.load('ultralytics/yolov5', 'yolov5s', device=1)

# Inference
threading.Thread(target=run, args=[model0, 'https://ultralytics.com/images/zidane.jpg'], daemon=True).start()
threading.Thread(target=run, args=[model1, 'https://ultralytics.com/images/bus.jpg'], daemon=True).start()

import torch

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)  # load pretrained
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False, pretrained=False)  # load scratch

results = model(im)  # inference

results.ims  # array of original images (as np array) passed to model for inference
results.render()  # updates results.ims with boxes and labels
for im in results.ims:
    buffered = BytesIO()
    im_base64 = Image.fromarray(im)
    im_base64.save(buffered, format="JPEG")
    print(base64.b64encode(buffered.getvalue()).decode('utf-8'))  # base64 encoded image with results

results = model(im)  # inference
crops = results.crop(save=True)  # cropped detections dictionary

results = model(im)  # inference
results.pandas().xyxy[0]  # Pandas DataFrame

results = model(im)  # inference
results.pandas().xyxy[0].sort_values('xmin')  # sorted left-right

results = model(im)  # inference
crops = results.crop(save=True)  # cropped detections dictionary

results = model(ims)  # inference
results.pandas().xyxy[0].to_json(orient="records")  # JSON img1 predictions

import torch

model = torch.hub.load('ultralytics/yolov5', 'custom', path='path/to/best.pt')  # local model
model = torch.hub.load('path/to/yolov5', 'custom', path='path/to/best.pt', source='local')  # local repo

import torch

model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.pt')  # PyTorch
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.torchscript')  # TorchScript
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.onnx')  # ONNX
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s_openvino_model/')  # OpenVINO
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.engine')  # TensorRT
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.mlmodel')  # CoreML (macOS-only)
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.tflite')  # TFLite
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s_paddle_model/')  # PaddlePaddle