yolo_v3

Yolo V3

You only look once (YOLO) is a state-of-the-art, real-time object detection system. On a Pascal Titan X it processes images at 30 FPS and has a mAP of 57.9% on COCO test-dev.

Description:

• Paper: YOLOv3: An Incremental Improvement (2018)
• Framework: Darknet
• Input resolution: 320x320, 416x416 (and other multiple of 32)
• Pretrained: COCO
• Weights size: ~250 mb
• Work modes: train, inference, deploy
• Usage example: Multi-class object detection using YOLO V3

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Architecture

Performance

Train configuration

General train configuration available in model presets.

Also you can read common training configurations documentation.

• lr - Learning rate.
• epochs - the count of training epochs.
• batch_size - batch sizes for training (train) stage.
• input_size - input images dimension width and height in pixels.
• bn_momentum - batch normalization momentum parameter.
• gpu_devices - list of selected GPU devices indexes.
• data_workers - how many subprocesses to use for data loading.
• dataset_tags - mapping for split data to train (train) and validation (val) parts by images tags. Images must be tagged by train or val tags.
• subdivisions - split batch on subbatches (if big batch size does not fit to GPU memory).
• special_classes - objects with specified classes will be interpreted in a specific way. Default class name for background is bg, default class name for neutral is neutral. All pixels from neutral objects will be ignored in loss function.
• print_every_iter - allow to output training information every N iterations.
• weights_init_type - can be in one of 2 modes. In transfer_learning mode all possible weights will be transfered except last layer. In continue_training mode all weights will be transfered and validation for classes number and classes names order will be performed.
• enable_augmentations - current implementation contains strong augmentation system. If you want to use it select true or false otherwise.

Full training configuration example:

{
    "lr": 0.0001,
    "epochs": 10,
    "batch_size": {
      "train": 8
    },
    "input_size": {
      "width": 416,
      "height": 416
    },
    "bn_momentum": 0.01,
    "gpu_devices": [0],
    "data_workers": {
      "train": 3
    },
    "dataset_tags": {
      "train": "train"
    },
    "subdivisions": {
      "train": 1
    },
    "print_every_iter": 10,
    "weights_init_type": "continue_training",
    "enable_augmentations": true
  }

Inference configuration

For full explanation see documentation.

model - group contains unique settings for each model:

• gpu_device - device to use for inference. Right now we support only single GPU.

• confidence_tag_name - name of confidence tag for predicted bound boxes.

mode - group contains all mode settings:

• name - mode name defines how to apply NN to image (e.g. full_image - apply NN to full image)

• model_classes - which classes will be used, e.g. NN produces 80 classes and you are going to use only few and ignore other. In that case you should set save_classes field with the list of interested class names. add_suffix string will be added to new class to prevent similar class names with exisiting classes in project. If you are going to use all model classes just set "save_classes": "__all__".

Full image inference configuration example:

{
  "model": {
    "gpu_device": 0,
    "confidence_tag_name": "confidence"
  },
  "mode": {
    "name": "full_image",
    "model_classes": {
      "save_classes": "__all__",
      "add_suffix": "_yolo"
    }
  }
}