Comments (5)
https://github.com/WongKinYiu/yolov7#training
from yolov7.
train demo
dataset: coco128,
set coco.yaml
train: images/train2017 # train images (relative to 'path') 128 images
val: images/train2017 # val images (relative to 'path') 128 images
set train.py
parser.add_argument('--weights', type=str, default='runs/yolov7.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='cfg/training/yolov7.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path')
parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path')
convert your dataset like coco128 or coco, either modify util/dataset.py read your dataset
from yolov7.
train demo
dataset: coco128,
set coco.yaml
train: images/train2017 # train images (relative to 'path') 128 images val: images/train2017 # val images (relative to 'path') 128 images
set train.py
parser.add_argument('--weights', type=str, default='runs/yolov7.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='cfg/training/yolov7.yaml', help='model.yaml path') parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path') parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path')
convert your dataset like coco128 or coco, either modify util/dataset.py read your dataset
As I understand, the custom dataset format like YOLOv5, you can download the dataset https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip to understand the dataset's structure. This link in the script [yolov7](https://github.com/WongKinYiu/yolov7)/[scripts](https://github.com/WongKinYiu/yolov7/tree/main/scripts)/get_coco.sh
from yolov7.
The training fails when set --rect flag. It seems that the labels/segments array length do not match in datasets when doing paste_in augmentation.
from yolov7.
The training fails when set --rect flag. It seems that the labels/segments array length do not match in datasets when doing paste_in augmentation.
Did you manage to solve the problem? I get recall 0 when training with --rect flag.
from yolov7.
Related Issues (20)
- Running train.py in evolve mode
- What is the difference between main brunch and u7 brunch?
- Terminating the python process while passing a tensor into a loaded model
- Alternative of NMS (torchvision.ops.nms(boxes, scores, iou_thres) or Modification Based on description
- David
- Normalize Confusion Matrix
- Is this an appropriate tool to use to detect images in live video feed? HOT 1
- How to get the name of the object from detection(like put it in a variable or smth)? HOT 1
- Is this repository still maintained? HOT 1
- If my custom dataset are all 300X300, or 600X600, is there necessary to train a model use image-size1280? HOT 4
- How to get segmentations in form of [ [670,35][6,305] [60,3]] from segments model of yolov7
- Don't detect a object in a image after prediction HOT 1
- question about ComputeLossOTA
- Hh
- onnx non end2end
- Problem wher run learning on 32 batch-size
- What exactly does image size do in test.py and detect.py?
- Change class weights HOT 1
- Memory Leak with PyTorch 2.3.1 and torchvision 0.18.1
- Using the train_aux.py training script, single gpu training fails, but multiple gpu training succeeds.
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from yolov7.