Open AlexeyAB opened 2 years ago
I tried to train a custom dataset with yolov7-tiny and conv.87 Unfortunately it gets stuck at a loss above 100. Same configuration in yolov4-tiny works great. Can you please help?
Are you going to launch the full yolov7.weights and yolov7.cfg? Or are there any option to convert it from .pt?
Good Morning, I'm using yolov7 to detect diseases in papaya, but the results are horrible. I have approximately 20k samples, divided into 8 diseases, the annotations are correct and I still get a max mAP of 34% (this after over 20,000 iterations). This result is slightly WORSE than yolov4, which achieves a mAP of 37%. Does anyone have an idea what could be wrong? *** With the efficientDet-d3 I get a mAP of 65% for the same base (I don't have the computational capacity to use the D6,D7..)
Attached are the yolov4/v7 configuration files and sample images
yolov4-custom_cfg.txt
yolov7-papaya_cfg.txt
@AlexeyAB @ESJavadex @jhony2507 I would also like to know what how do I get the weights file for yolov7? Can I convert .pt
to .weights
somehow? (I guess not)
How do I train .weights
then? How is it different from training V3/V4? 🙏
I guess we can convert .pt
to . onnx
though. 🤔
I have the yolov7-w6 weights file in darknet format best.weights, Is there any way to convert it in pt. Also, is there a yolov7-W6 config file available (yolov7-w6.cfg)
Official YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
paper: https://arxiv.org/abs/2207.02696
source code - Pytorch (use to reproduce results): https://github.com/WongKinYiu/yolov7
Darknet cfg/weights file - currently tested for inference only:
Test FPS on: https://github.com/AlexeyAB/darknet
without NMS:
darknet.exe detector demo cfg/coco.data yolov7-tiny.cfg yolov7-tiny.weights test.mp4 -benchmark
with NMS:
darknet.exe detector demo cfg/coco.data yolov7-tiny.cfg yolov7-tiny.weights test.mp4 -dont_show
YOLOv7 is more accurate and faster than YOLOv5 by 120% FPS, than YOLOX by 180% FPS, than Dual-Swin-T by 1200% FPS, than ConvNext by 550% FPS, than SWIN-L by 500% FPS, than PPYOLOE-X by 150% FPS.
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100, batch=1.
+500%
FPS faster than SWIN-L C-M-RCNN (53.9% AP, 9.2 FPS A100 b=1)+550%
FPS faster than ConvNeXt-XL C-M-RCNN (55.2% AP, 8.6 FPS A100 b=1)+120%
FPS faster than YOLOv5-X6-r6.1 (55.0% AP, 38 FPS V100 b=1)+1200%
FPS faster than Dual-Swin-T C-M-RCNN (53.6% AP, 6.5 FPS V100 b=1)+150%
FPS faster than PPYOLOE-X (51.9% AP, 45 FPS V100 b=1)+180%
FPS faster than YOLOX-X (51.1% AP, 58 FPS V100 b=1)