Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Question mAP #27

Open
Mirorrn opened this issue Oct 19, 2017 · 12 comments
Open

Question mAP #27

Mirorrn opened this issue Oct 19, 2017 · 12 comments

Comments

@Mirorrn
Copy link

Mirorrn commented Oct 19, 2017

Hi, thanks for posting your code :)

I have a question about mAP. I dont understand how to compute this metric.
I saw that you calc it but can´t find it in your code.

Thank you!

@experiencor
Copy link
Owner

I have yet to commit the evaluation code. It's very messy now. But it's coming soon.

P/S: I modify the code from https://github.com/cocodataset/cocoapi/.

@Mirorrn
Copy link
Author

Mirorrn commented Oct 20, 2017

I have written my own code based on VOC evaluation. But i think my results are wrong. My mAP results for original tiny Yolo on VOC 2007 is 44 and not 57 :(
Maybe because i don't sort my results for highest confidenz score at first.

@alessandro-montanari
Copy link

I am using this implementation for mAP https://github.com/MathGaron/mean_average_precision (at the moment is not interpolated and by default it computes the precision-recall curve in 11 points).
For simplicity, instead of using it as a Keras metric I use the predict method to get predictions for some images and then update the mAP metric with evaluate.
The structure is something like this:

yolo = YOLO(...)  # Create model
yolo.load_weights(weights_path)  # Load weights
mAP = DetectionMAP(num_classes)  # Initialise metric
for image in images:
    boxes = yolo.predict(image)
    # prepare objects pred_bb, pred_classes, pred_conf, gt_bb and gt_classes
    mAP.evaluate(pred_bb, pred_classes, pred_conf, gt_bb, gt_classes)  # Update the metric

mAP.plot()  # Get the value of the metric and precision-recall plot for each class

It seems it's working fine so it might be helpful for somebody.
BTW, @experiencor thanks for the Yolo implementation :)

@sivanagendra123
Copy link

Sir I have been trained it after 3 ephocs it gives me error as killed and loss not yet decreased.how would I fix these in code "after 3 ephocs it killed", but I want it doesn't kill after 3 ephocs.

@chris2009
Copy link

Hi! sir nice contribute,

I have a question: ther train.py go 8 hours in maximun CVPU and memory, is it normal?

I wait your kind feedback.
Captura de pantalla de 2019-04-06 09-52-39

Captura de pantalla de 2019-04-06 02-13-51

@mrannadinc
Copy link

is this normal? the training scale is 15 hours left and my 1 epoch is steel like this i cant understand why?
Ekran Görüntüsü (27)

somebody can help me?

@isennkubilay
Copy link

I have yet to commit the evaluation code. It's very messy now. But it's coming soon.

P/S: I modify the code from https://github.com/cocodataset/cocoapi/.

Hi,
Did you commit the evaluation code?

@MuhammadAsadJaved
Copy link

I am using this implementation for mAP https://github.com/MathGaron/mean_average_precision (at the moment is not interpolated and by default it computes the precision-recall curve in 11 points).
For simplicity, instead of using it as a Keras metric I use the predict method to get predictions for some images and then update the mAP metric with evaluate.
The structure is something like this:

yolo = YOLO(...)  # Create model
yolo.load_weights(weights_path)  # Load weights
mAP = DetectionMAP(num_classes)  # Initialise metric
for image in images:
    boxes = yolo.predict(image)
    # prepare objects pred_bb, pred_classes, pred_conf, gt_bb and gt_classes
    mAP.evaluate(pred_bb, pred_classes, pred_conf, gt_bb, gt_classes)  # Update the metric

mAP.plot()  # Get the value of the metric and precision-recall plot for each class

It seems it's working fine so it might be helpful for somebody.
BTW, @experiencor thanks for the Yolo implementation :)

@alessandro-montanari can you help me to calculate mAP using that repo? i do not understand it . Can you explain me which files i need to prepare to run? i have trained keras_yolo2 model using thsi repo. now i want to calculate mAP using this model. which other files i need and how to run the other repo for mAP? Can you provide your email address?

@schliffen
Copy link

@mrannadinc yes that is normal! work with training parameters; learning rate etc.

@schliffen
Copy link

Did anyone tried squeezenet? I trained squeezenet, the training went fine, in converged and loss dropped to 0.01, but when I trying to test it, nothing is detected. Did anyone experience this?
Q2: what would be the input size for squeezenet? 224 or 416 or sthing else?

--thankyou

@kishoremohit
Copy link

Hello,

I trained yolov2 using all explanations with 12 classes but it is detecting only 8 classes but not all 12. Can anyone please help me with increasing the accuracy of the yolov2 model.

Hoping for the reply as soon as possible.

TIA

@MuhammadAsadJaved
Copy link

MuhammadAsadJaved commented Apr 2, 2020 via email

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

No branches or pull requests

10 participants