Darknet-19 YOLO about mobilenet-yolo HOT 11 CLOSED

Hi ,
Thanks for your feedback , I'll try it next week , here is my guess to solve this problem :

1. to get a good pre-train weights
2. solver type change to sgd

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I first try to train original model and using pretrain coco weights (conver from original website , tutorial ), and mAP was 0.72 , deploy model just uploaded , there are some reasons mAP be lower than original yolo :

1. Original concat layer fixed shape when layer setup , so training can not use adaptive image size, like 320~608
2. I can't convert voc weights to caffemodel , I think there are some different between these frameworks
3. etc ..

And if have time and machine , I'll try to optimize it.

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Hi,

Thanks for your reply.
I have tried some modifications on the darknet19+yolov2 architecture.
Can you briefly describe how to test the mAP after training?
I used the following command but only returned some weird information...
build/tools/caffe test --model yolo_darknet_test.prototxt --weights yolo_darknet_solver_iter_80000.caffemodel --iterations 4952 --gpu 0

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I usually use to set solver max_iter = 0 , and start training . It should start to test
Note : do not modify batch size of test prototxt , currently , the yolo eval function do not support batch size > 1

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Actually, I have tried so...
But got no response...

Here is my solver setting

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Below is my solver setting , and it works ,
I think if it do not show mAP , maybe eval layer didn't receive any detection results . It happened when used mismatch prototxt and model

Note : I update the darknet model today

train_net: "models/darknet/darknet19_train.prototxt"
test_net: "models/darknet/darknet19_test.prototxt"
test_iter: 4952
test_interval: 1000
base_lr: 0.001
display: 10
max_iter: 0
lr_policy: "multistep"
gamma: 0.1
weight_decay: 0.00005
snapshot: 1000
snapshot_prefix: "models/darknet/yolov2_deploy"
solver_mode: GPU
debug_info: false
snapshot_after_train: true
test_initialization: false
average_loss: 10
stepvalue: 60000
stepvalue: 80000
iter_size: 2
type: "SGD"
eval_type: "detection"
ap_version: "11point"
show_per_class_result: true

script :
./tools/caffe train --solver models/darknet/darknet19_solver.prototxt --weights models/darknet/yolov2_deploy_iter_50000.caffemodel --gpu=0 2>&1 | tee $LOG

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Hi,

I trained the darknet-yolov2 with the pre-trained weights(darknet-19, 448 in imagenet, not coco) released here, and convert to caffe format with this repo.
I have tried many kinds of parameter setting but the best mAP that I can reach is less than 60%, which is much lower than 76%. I have sent you the model to you via your gmail, can you help inspect what is going wrong in my training procedure?

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I'm not sure , but I don't have machine can try to training now , and below is my guess :

1. Transform param in test.prototxt was
   scale: 0.007843
   mean_value: 127.5
   mean_value: 127.5
   mean_value: 127.5

   But in the train.prototxt was gone in your prototxt , and I suggest scale set to 1/255.5 and mean set to
   zero

2. I try to convert darknet.cov23 to caffemodel yet , but count was dismatch, I didn't solve this problem ,
   and I can not ensure the caffemodel was correct

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Hi,

Sorry to bother you again...

1. I found that I sent you the wrong train.prototxt. It does have transform param settings in my experiments, actually. The darknet pre-processing scales the pixel value into [-1,+1], so I think it is right to set the transform param as:
   scale: 0.007843
   mean_value: 127.5
   mean_value: 127.5
   mean_value: 127.5

2. I think the converted weights with this repo is correct for I have tried the converted weights for other architectures and it works.

Thank you

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Ok , Maybe I'll need train this model and try to find the problems .
Thanks for your feedback

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I update a new mode training from darknet19.conv , can only get 0.71

https://github.com/eric612/Caffe-YOLOv2-Windows/tree/master/models/darknet

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