train_detectron2_detection

Train your custom Detectron2 object detection models.

🚀 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

# Init your workflow
wf = Workflow()    

# Add dataset loader
coco = wf.add_task(name="dataset_coco")

coco.set_parameters({
    "json_file": "path/to/annotation/file.json",
    "image_folder": "path/to/image/folder",
    "task": "detection",
}) 

# Add train algorithm
train = wf.add_task(name="train_detectron2_detection", auto_connect=True)

# Launch your training on your data
wf.run()

☀️ 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 'COCO-Detection/faster_rcnn_R_101_FPN_3x': Name of the pre-trained model. Additional model available:

  • COCO-Detection/faster_rcnn_R_101_C4_3x
  • COCO-Detection/faster_rcnn_R_101_DC5_3x
  • COCO-Detection/faster_rcnn_R_101_FPN_3x
  • COCO-Detection/faster_rcnn_R_50_C4_1x
  • COCO-Detection/faster_rcnn_R_50_C4_3x
  • COCO-Detection/faster_rcnn_R_50_DC5_1x
  • COCO-Detection/faster_rcnn_R_50_DC5_3x
  • COCO-Detection/faster_rcnn_R_50_FPN_1x
  • COCO-Detection/faster_rcnn_R_50_FPN_3x
  • COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x
  • COCO-Detection/fast_rcnn_R_50_FPN_1x
  • COCO-Detection/retinanet_R_101_FPN_3x
  • COCO-Detection/retinanet_R_50_FPN_1x
  • COCO-Detection/retinanet_R_50_FPN_3x
  • PascalVOC-Detection/faster_rcnn_R_50_C4

• max_iter (int) - default '100': Maximum number of iterations.

• batch_size (int) - default '2': Number of samples processed before the model is updated.

• input_size (int) - default '400': Size of the input image.

• output_folder (str, optional): path to where the model will be saved.

• learning_rate (float) - default '0.0025': Step size at which the model's parameters are updated during training.

• eval_period (int): Interval between evalutions.

• dataset_split_ratio (float) – default '0.8' ]0, 1[: Divide the dataset into train and evaluation sets.

• config_file(str, optional): Path to config file.

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

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()    

# Add dataset loader
coco = wf.add_task(name="dataset_coco")

coco.set_parameters({
    "json_file": "path/to/annotation/file.json",
    "image_folder": "path/to/image/folder",
    "task": "detection",
}) 

# Add train algorithm
train = wf.add_task(name="train_detectron2_detection", auto_connect=True)
train.set_parameters({
    "model_name": "COCO-Detection/faster_rcnn_R_50_C4_1x",
    "batch_size": "2",
    "input_size": "400",
    "learning_rate": "0.0025",
    "dataset_split_ratio": "0.8",
}) 

# Launch your training on your data
wf.run()