MobileNet-V4 (now in timm)
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Ross Wightman
rwightman
AI & ML interests
Computer vision, transfer learning, semi/self supervised learning, robotics.
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MobileNetV4 weights are now in timm! So far these are the only weights for these models as the offiicial Tensorflow impl remains weightless.
Guided by paper hparams with a few tweaks, I've managed to match or beat the paper results training the medium models. I'm still working on large and improving the small result. They appear to be solid models for on-device use.
timm/mobilenetv4-pretrained-weights-6669c22cda4db4244def9637
MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)
Guided by paper hparams with a few tweaks, I've managed to match or beat the paper results training the medium models. I'm still working on large and improving the small result. They appear to be solid models for on-device use.
timm/mobilenetv4-pretrained-weights-6669c22cda4db4244def9637
MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)
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1329
Aiming to keep
Paper: MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)
timm (https://github.com/huggingface/pytorch-image-models) the go-to library for efficient image encoders for your mobile and edge devices, I've started working on an implementation of the new MobileNet-V4 model. Take a look at a short article I wrote about the model: https://huggingface.co/blog/rwightman/mobilenetv4Paper: MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)
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Fine-tune experiments for various `timm` models on the INaturalist 2021 Challenge dataset (https://github.com/visipedia/inat_comp/tree/master/2021)
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timm/eva02_large_patch14_clip_336.merged2b_ft_inat21
Image Classification • Updated • 105 • 11 -
timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_inat21
Image Classification • Updated • 61 • 1 -
timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21
Image Classification • Updated • 50 -
timm/vit_large_patch14_clip_336.datacompxl_ft_inat21
Image Classification • Updated • 54 • 1
