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Fine-tuning YOLO-World

Fine-tuning YOLO-World is easy and we provide the samples for COCO object detection as a simple guidance.

Fine-tuning Requirements

Fine-tuning YOLO-World is cheap:

  • it does not require 32 GPUs for multi-node distributed training. 8 GPUs or even 1 GPU is enough.

  • it does not require the long schedule, e.g., 300 epochs or 500 epochs for training YOLOv5 or YOLOv8. 80 epochs or fewer is enough considering that we provide the good pre-trained weights.

Data Preparation

The fine-tuning dataset should have the similar format as the that of the pre-training dataset. We suggest you refer to docs/data for more details about how to build the datasets:

  • if you fine-tune YOLO-World for close-set / custom vocabulary object detection, using MultiModalDataset with a text json is preferred.

  • if you fine-tune YOLO-World for open-vocabulary detection with rich texts or grounding tasks, using MixedGroundingDataset is preferred.

Hyper-parameters and Config

Please refer to the config for fine-tuning YOLO-World-L on COCO for more details.

  1. Basic config file:

If the fine-tuning dataset contains mask annotations:

_base_ = ('../../third_party/mmyolo/configs/yolov8/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py')

If the fine-tuning dataset doesn't contain mask annotations:

_base_ = ('../../third_party/mmyolo/configs/yolov8/yolov8_l_syncbn_fast_8xb16-500e_coco.py')
  1. Training Schemes:

Reducing the epochs and adjusting the learning rate

max_epochs = 80
base_lr = 2e-4
weight_decay = 0.05
train_batch_size_per_gpu = 16
close_mosaic_epochs=10

train_cfg = dict(
    max_epochs=max_epochs,
    val_interval=5,
    dynamic_intervals=[((max_epochs - close_mosaic_epochs),
                        _base_.val_interval_stage2)])
  1. Datasets:
coco_train_dataset = dict(
    _delete_=True,
    type='MultiModalDataset',
    dataset=dict(
        type='YOLOv5CocoDataset',
        data_root='data/coco',
        ann_file='annotations/instances_train2017.json',
        data_prefix=dict(img='train2017/'),
        filter_cfg=dict(filter_empty_gt=False, min_size=32)),
    class_text_path='data/texts/coco_class_texts.json',
    pipeline=train_pipeline)

Finetuning without RepVL-PAN or Text Encoder 🚀

For further efficiency and simplicity, we can fine-tune an efficient version of YOLO-World without RepVL-PAN and the text encoder. The efficient version of YOLO-World has the similar architecture or layers with the orignial YOLOv8 but we provide the pre-trained weights on large-scale datasets. The pre-trained YOLO-World has strong generalization capabilities and is more robust compared to YOLOv8 trained on the COCO dataset.

You can refer to the config for Efficient YOLO-World for more details.

The efficient YOLO-World adopts EfficientCSPLayerWithTwoConv and the text encoder can be removed during inference or exporting models.

model = dict(
    type='YOLOWorldDetector',
    mm_neck=True,
    neck=dict(type='YOLOWorldPAFPN',
              guide_channels=text_channels,
              embed_channels=neck_embed_channels,
              num_heads=neck_num_heads,
              block_cfg=dict(type='EfficientCSPLayerWithTwoConv')))

Launch Fine-tuning!

It's easy:

./dist_train.sh <path/to/config> <NUM_GPUS> --amp

COCO Fine-tuning

model efficient neck AP AP50 AP75 weights
YOLO-World-S ✖️ 45.7 62.3 49.9 comming
YOLO-World-M ✖️ 50.7 67.2 55.1 comming
YOLO-World-L ✖️ 53.3 70.3 58.1 comming
YOLO-World-S ✔️ 45.9 62.3 50.1 comming
YOLO-World-M ✔️ 51.2 68.1 55.9 comming
YOLO-World-L ✔️ 53.3 70.1 58.2 comming