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A 'small' MobileNet-V4 update, I just pushed weights for the smallest model I've trained in the series, a 0.5 width multiplier version of the MobileNet-V4 Conv Small.
A 'small' MobileNet-V4 update, I just pushed weights for the smallest model I've trained in the series, a 0.5 width multiplier version of the MobileNet-V4 Conv Small.
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Now you may look at this and say hey, why is this impressive? 64.8% top-1 and 2.2M params? MobileNetV3-Small 0.75, and MobileNet-V2 0.5 are both fewer params (at ~2M) and over 65% top-1, what gives? Well this is where MobileNet-V4 differs from the previous versions of the model family, it trades off (gives up) a little parameter efficiency for some computational efficiency.
Now you may look at this and say hey, why is this impressive? 64.8% top-1 and 2.2M params? MobileNetV3-Small 0.75, and MobileNet-V2 0.5 are both fewer params (at ~2M) and over 65% top-1, what gives? Well this is where MobileNet-V4 differs from the previous versions of the model family, it trades off (gives up) a little parameter efficiency for some computational efficiency.
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So, let's look at the speed. On a 4090 w/ torchcompile
So, let's look at the speed. On a 4090 w/ torchcompile
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* 98K img/sec - 
* 98K img/sec - 
resource
https://huggingface.co/timm/mobilenetv4_conv_small_050.e3000_r224_in1k
model
timm/mobilenetv4_conv_small_050.e3000_r224_in1k
https://huggingface.co/timm/mobilenetv4_conv_small_050.e3000_r224_in1k
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* 58K img/sec - 
* 58K img/sec - 
resource
https://huggingface.co/timm/mobilenetv3_small_075.lamb_in1k
model
timm/mobilenetv3_small_075.lamb_in1k
https://huggingface.co/timm/mobilenetv3_small_075.lamb_in1k
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* 37K img/sec - 
* 37K img/sec - 
resource
https://huggingface.co/timm/mobilenetv2_050.lamb_in1k
model
timm/mobilenetv2_050.lamb_in1k
https://huggingface.co/timm/mobilenetv2_050.lamb_in1k
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And there you go, if you have a need for speed, MNV4 is the better option.
And there you go, if you have a need for speed, MNV4 is the better option.
A 'small' MobileNet-V4 update, I just pushed weights for the smallest model I've trained in the series, a 0.5 width multiplier version of the MobileNet-V4 Conv Small.

Now you may look at this and say hey, why is this impressive? 64.8% top-1 and 2.2M params? MobileNetV3-Small 0.75, and MobileNet-V2 0.5 are both fewer params (at ~2M) and over 65% top-1, what gives? Well this is where MobileNet-V4 differs from the previous versions of the model family, it trades off (gives up) a little parameter efficiency for some computational efficiency.

So, let's look at the speed. On a 4090 w/ torchcompile
* 98K img/sec - https://huggingface.co/timm/mobilenetv4_conv_small_050.e3000_r224_in1k
* 58K img/sec - https://huggingface.co/timm/mobilenetv3_small_075.lamb_in1k
* 37K img/sec - https://huggingface.co/timm/mobilenetv2_050.lamb_in1k

And there you go, if you have a need for speed, MNV4 is the better option.
❤️
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