New MobileNetV4 weights were uploaded a few days ago -- more ImageNet-12k training at 384x384 for the speedy 'Conv Medium' models.
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New MobileNetV4 weights were uploaded a few days ago -- more ImageNet-12k training at 384x384 for the speedy 'Conv Medium' models.
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There are 3 weight variants here for those who like to tinker. On my hold-out eval they are ordered as below, not that different, but the Adopt 180 epochs closer to AdamW 250 than to AdamW 180.
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There are 3 weight variants here for those who like to tinker. On my hold-out eval they are ordered as below, not that different, but the Adopt 180 epochs closer to AdamW 250 than to AdamW 180.
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 * AdamW for 250 epochs - 
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 * AdamW for 250 epochs - 
https://huggingface.co/timm/mobilenetv4_conv_medium.e250_r384_in12k
timm/mobilenetv4_conv_medium.e250_r384_in12k
model
resource
https://huggingface.co/timm/mobilenetv4_conv_medium.e250_r384_in12k
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 * Adopt for 180 epochs - 
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 * Adopt for 180 epochs - 
https://huggingface.co/timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
model
resource
https://huggingface.co/timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
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 * AdamW for 180 epochs - 
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 * AdamW for 180 epochs - 
https://huggingface.co/timm/mobilenetv4_conv_medium.e180_r384_in12k
timm/mobilenetv4_conv_medium.e180_r384_in12k
model
resource
https://huggingface.co/timm/mobilenetv4_conv_medium.e180_r384_in12k
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This was by request as a user reported impressive results using the 'Conv Large' ImagNet-12k pretrains as object detection backbones. ImageNet-1k fine-tunes are pending, the weights do behave differently with the 180 vs 250 epochs and the Adopt vs AdamW optimizer. 
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This was by request as a user reported impressive results using the 'Conv Large' ImagNet-12k pretrains as object detection backbones. ImageNet-1k fine-tunes are pending, the weights do behave differently with the 180 vs 250 epochs and the Adopt vs AdamW optimizer. 
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New MobileNetV4 weights were uploaded a few days ago -- more ImageNet-12k training at 384x384 for the speedy 'Conv Medium' models.

There are 3 weight variants here for those who like to tinker. On my hold-out eval they are ordered as below, not that different, but the Adopt 180 epochs closer to AdamW 250 than to AdamW 180.
 * AdamW for 250 epochs - https://huggingface.co/timm/mobilenetv4_conv_medium.e250_r384_in12k
 * Adopt for 180 epochs - https://huggingface.co/timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
 * AdamW for 180 epochs - https://huggingface.co/timm/mobilenetv4_conv_medium.e180_r384_in12k

This was by request as a user reported impressive results using the 'Conv Large' ImagNet-12k pretrains as object detection backbones. ImageNet-1k fine-tunes are pending, the weights do behave differently with the 180 vs 250 epochs and the Adopt vs AdamW optimizer. 


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