How to initial weight without pretrain?

beebrain commented 3 years ago

❔Question

I would like to initial weight without pre-train or transfer weight. I try to set --weight '' but it doesn't work. Please suggest me.

Additional context

glenn-jocher commented 3 years ago

Ultralytics has open-sourced YOLOv5 at https://github.com/ultralytics/yolov5, featuring faster, lighter and more accurate object detection. YOLOv5 is recommended for all new projects.

** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from google/automl at batch size 8.

August 13, 2020: v3.0 release: nn.Hardswish() activations, data autodownload, native AMP.
July 23, 2020: v2.0 release: improved model definition, training and mAP.
June 22, 2020: PANet updates: new heads, reduced parameters, improved speed and mAP 364fcfd.
June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
May 27, 2020: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
April 1, 2020: Start development of future compound-scaled YOLOv3/YOLOv4-based PyTorch models.

Pretrained Checkpoints

Model	AP^val	AP^test	AP₅₀	Speed_GPU	FPS_GPU		params
YOLOv5s	37.0	37.0	56.2	2.4ms	416	7.5M	13.2B
YOLOv5m	44.3	44.3	63.2	3.4ms	294	21.8M	39.4B
YOLOv5l	47.7	47.7	66.5	4.4ms	227	47.8M	88.1B
YOLOv5x	49.2	49.2	67.7	6.9ms	145	89.0M	166.4B

YOLOv5x + TTA	50.8	50.8	68.9	25.5ms	39	89.0M	354.3B

YOLOv3-SPP	45.6	45.5	65.2	4.5ms	222	63.0M	118.0B

AP^test denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.
All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.001
Speed_GPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1
All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). Test Time Augmentation (TTA) runs at 3 image sizes. Reproduce** by python test.py --data coco.yaml --img 832 --augment

For more information and to get started with YOLOv5 please visit https://github.com/ultralytics/yolov5. Thank you!

WZMIAOMIAO commented 3 years ago

change train.py(L117):

if weights.endswith('.pt'):

to

if False:

github-actions[bot] commented 3 years ago

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

glenn-jocher commented 10 months ago

@WZMIAOMIAO changing the condition from if weights.endswith('.pt') to if False will prevent the weights from loading. However, this modification is not recommended as it may lead to unexpected behavior, and it's always best to use the standard approach for weight initialization. If you have specific requirements for weight initialization, please feel free to share more details so that we can offer the most suitable guidance.

ultralytics / yolov3