infer_yolo_v7

infer_yolo_v7

About

1.2.1
GPL-3.0

YOLOv7 object detection models.

Task: Object detection
yolo
v7
object
detection
real-time
coco

Run YOLOv7 object detection models.

Desk object detection

🚀 Use with Ikomia API

1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.

pip install ikomia

2. Create your workflow

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_yolo_v7", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")

# Inpect your result
display(algo.get_image_with_graphics())

☀️ Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

  • If you haven't started using Ikomia Studio yet, download and install it from this page.

  • For additional guidance on getting started with Ikomia Studio, check out this blog post.

📝 Set algorithm parameters

  • model_name (str) - default 'yolov7': Name of the YOLOv7 pre-trained model. Other model available:

    • yolov7x
    • yolov7-w6
    • yolov7-e6
    • yolov7-e6e
  • conf_thres (float) default '0.25': Box threshold for the prediction [0,1].

  • input_size (int) - default '640': Size of the input image.

  • iou_thres (float) - default '0.5': Intersection over Union, degree of overlap between two boxes [0,1].

  • cuda (bool): If True, CUDA-based inference (GPU). If False, run on CPU.

  • model_weight_file (str, optional): Path to model weights file .pt.

Parameters should be in strings format when added to the dictionary.

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_yolo_v7", auto_connect=True)

algo.set_parameters({
    "model_name": "yolov7",
    "conf_thres": "0.5",
    "input_size": "640",
    "iou_thres": "0.5",
    "cuda": "True"
})

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")

# Inpect your result
display(algo.get_image_with_graphics())

🔍 Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the documentation.

import ikomia
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_yolo_v7", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")

# Iterate over outputs
for output in algo.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()

Developer

  • Ikomia
    Ikomia

License

GNU General Public License v3.0
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