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How is the result?
Training the network from scratch
@thedeepreader Hi, I am training my network from scratch using resnet-50 backbone with imagenet pretrained weight on my custom dataset. The network is not converging, can you please help me in this regard. Below are my training details and plots of logs.
Backbone : resnet-50
learning_rate = 1e-4
learning_rate_backbone=1e-5
eos_coeff = 0.1
num_queries = 20
lerarning_rate_drop = 100
error at line 157 datasets/face.py
function
make_Face_transforms(image_set) ---->make_face_transforms(image_set)
Extracting mAP for each class when evaluating on custom dataset
@thedeepreader I am using a custom COCO dataset.
Is there some way to extract mAP metric for each individual class/label when evaluating?
For example, I have 2 classes of the same object category. How do I address this in code to allow individual class results during inference?
Any help or advice is appreciated, thanks!
How are we going to change this part of the code if we have more than 1 class?
"
categories = [
{
"supercategory": "none",
"name": "face",
"id": 0
}
]
"
How do I get the accuracy of DETR model?
Data augmentation for custom training.
Dear Sirs,
Thanks to your tutorial, checkpoint file for my custom dataset was obtained.
To get more accuracy, I would like to add data augmentation during training.
From experience, "RGB to GREYSCALE" and "ROTATE 90" might be tried.
Would you please advice how to add above 2 augmentations.
Thank in advance.
AssertionError "..detr\util\box_ops.py", line 51, in generalized_box_iou assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
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