using mobilenetv3 + yolov5

[lana211191]

Disclaimer: I am a bit of a novice , i started learning deep learning 7 month ago so i might have some knowledge gaps

Hello, i want to use Mobilenetv3 with yolov5, i adjusted the code but i have an issue.
I understand that now that i changed theh backbone to mobilenet i cannot use the yolov5 pretrained weights since there is no common/similar type of layers in the backbone.

i was wondering if i can use mobilenet v3 pretrained weights (downloaded from pytorch) and freeze a part of the layers ?

If yes how can i transform the .pth file of mobilenet to .pt (accepted by yolov5)
(error that i get: mobilenet_v3_large-8738ca79.pth acceptable suffix is ['.pt'])
Thank you

[github-actions]

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[glenn-jocher]

@lana211191 YOLOv5 doesn't recognize *.pth suffixes which is causing your error. You can simply rename your file to end with *.pt to avoid this.

Attempting to transfer weights is a different story, you'd have to implement this feature yourself in train.py here:

yolov5/train.py

Lines 117 to 132 in 601dbb8

	# Model
	check_suffix(weights, '.pt') # check weights
	pretrained = weights.endswith('.pt')
	if pretrained:
	with torch_distributed_zero_first(LOCAL_RANK):
	weights = attempt_download(weights) # download if not found locally
	ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak
	model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
	exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys
	csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
	csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
	model.load_state_dict(csd, strict=False) # load
	LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report
	else:
	model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create

[zhiqwang]

FYI @lana211191 and @glenn-jocher ,

We propose a new way to construct a YOLOv5 Lite model with TorchVision's pre-trained MobileNetV3-Large FPN backbone. Concretely we restructured the YOLOv5's model into following four sub-modules in the layout of TorchVision, so it could better load the pre-trained models published by TorchVision. Maybe this can also used as a plugin for YOLOv5. See zhiqwang/yolort#342 and zhiqwang/yolort#343 for more details.

• BackboneWithPAN
• YOLOHead
• PostProcess
• SetCriterion

The SetCriterion is almost same with YOLOv5's ComputeLoss, the only differences is the output type.

[github-actions]

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