full connect about yolo_v1_pytorch HOT 6 CLOSED

OK, I have understood, thank you very much!

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Hi, It should be 1,000.

After pre-trained Darknet on ImageNet (with 1,000 classes), only the convolution layers (layers except for the FC-layer) are passed to YOLO model as you can see in the implementation below.

In YOLO model, a new FC-layer is defined and it has 20 classes.

from yolo_v1_pytorch.

Hi, It should be 1,000.

After pre-trained Darknet on ImageNet (with 1,000 classes), only the convolution layers (layers except for the FC-layer) are passed to YOLO model as you can see in the implementation below.

yolo_v1_pytorch/train_yolo.py

Line 79 in c3e60d7

yolo = YOLOv1(darknet.features)

In YOLO model, a new FC-layer is defined and it has 20 classes.

from yolo_v1_pytorch.

Hi, thank you very much,
it's me didn't understand completely, I print the model. features find is only have convolution layers, thank you!

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But I confusion, when we training the model in VOC data sets, using the "train_darknet.py", the function of "_make_fc_layers" in "darknet.py" shouldn't change to 20? (because I understand, we training in VOC data sets, not in ImageNet data sets, that only 20 classes, not 1000), it's somewhere I didn't understand? could you give me a more explanation? thank you

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train_darknet.py has nothing to with VOC. It should run with ImageNet dataset because Darknet is firstly pre-trained on ImageNet as written in the original YOLO paper.

After pre-trained Darket on ImageNet, its feature extraction part (convolution layers) and YOLO head FC-layer are further fine-tuned on VOC dataset.

Maybe I should have prepared README explaining the procedure but I did not have enough time..

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