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📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub

Before You Start

Start from a Python>=3.8 environment with PyTorch>=1.7 installed, as well as pyyaml>=5.3 for reading YOLOv5 configuration files. To install PyTorch see To install YOLOv5 requirements:

$ pip install -r

Cloning the repository is not required 😃.

Load YOLOv5 with PyTorch Hub

Simple Example

This example loads a pretrained YOLOv5s model from PyTorch Hub as model and passes an image for inference. 'yolov5s' is the lightest and fastest YOLOv5 model. For details on all available models please see the README.

import torch

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

# Image
img = ''

# Inference
results = model(img)

Detailed Example

This example shows batched inference with PIL and OpenCV image sources. results can be printed to console, saved to runs/hub, showed to screen on supported environments, and returned as tensors or pandas dataframes.

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('' + f, f)  # download 2 images
img1 ='zidane.jpg')  # PIL image
img2 = cv2.imread('bus.jpg')[:, :, ::-1]  # OpenCV image (BGR to RGB)
imgs = [img1, img2]  # batch of images

# Inference
results = model(imgs, size=640)  # includes NMS

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

results.xyxy[0]  # img1 predictions (tensor)
results.pandas().xyxy[0]  # img1 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

For all inference options see YOLOv5 autoShape() forward method:

Inference Settings

Inference settings such as confidence threshold, NMS IoU threshold, and classes filter are model attributes, and can be modified by:

model.conf = 0.25  # confidence threshold (0-1)
model.iou = 0.45  # NMS IoU threshold (0-1)
model.classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for persons, cats and dogs

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

Input Channels

To load a pretrained YOLOv5s model with 4 input channels rather than the default 3:

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

In this case the model will be composed of pretrained weights except for the very first input layer, which is no longer the same shape as the pretrained input layer. The input layer will remain initialized by random weights.

Number of Classes

To load a pretrained YOLOv5s model with 10 output classes rather than the default 80:

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

In this case the model will be composed of pretrained weights except for the output layers, which are no longer the same shape as the pretrained output layers. The output layers will remain initialized by random weights.

Force Reload

If you run into problems with the above steps, setting force_reload=True may help by discarding the existing cache and force a fresh download of the latest YOLOv5 version from PyTorch Hub.

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


To load a YOLOv5 model for training rather than inference, set autoshape=False. To load a model with randomly initialized weights (to train from scratch) use pretrained=False.

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

Base64 Results

For use with API services. See and Flask REST API example for details.

results = model(imgs)  # inference

results.imgs # array of original images (as np array) passed to model for inference
results.render()  # updates results.imgs with boxes and labels
for img in results.imgs:
    buffered = BytesIO()
    img_base64 = Image.fromarray(img), format="JPEG")
    print(base64.b64encode(buffered.getvalue()).decode('utf-8'))  # base64 encoded image with results

JSON Results

Results can be returned in JSON format once converted to .pandas() dataframes using the .to_json() method. The JSON format can be modified using the orient argument. See pandas .to_json() documentation for details.

results = model(imgs)  # inference

results.pandas().xyxy[0].to_json(orient="records")  # JSON img1 predictions
JSON Output (click to expand) ```json [ {"xmin":749.5,"ymin":43.5,"xmax":1148.0,"ymax":704.5,"confidence":0.8740234375,"class":0,"name":"person"}, {"xmin":433.5,"ymin":433.5,"xmax":517.5,"ymax":714.5,"confidence":0.6879882812,"class":27,"name":"tie"}, {"xmin":115.25,"ymin":195.75,"xmax":1096.0,"ymax":708.0,"confidence":0.6254882812,"class":0,"name":"person"}, {"xmin":986.0,"ymin":304.0,"xmax":1028.0,"ymax":420.0,"confidence":0.2873535156,"class":27,"name":"tie"} ] ```

Custom Models

This example loads a custom 20-class VOC-trained YOLOv5s model '' with PyTorch Hub.

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


YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):


CI CPU testing

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (, testing (, inference ( and export ( on MacOS, Windows, and Ubuntu every 24 hours and on every commit.