Which is the fastest image pretrained model?
$begingroup$
I had been working with pre-trained models and was just curious to know the fastest forward propagating model of all the computer vision pre-trained models. I have been trying to achieve faster processing in one-shot learning and have tried the forward propagation with few models over a single image and the results are as follows:
VGG16: 4.857 seconds
ResNet50: 0.227 seconds
Inception: 0.135 seconds
Can you tell the fastest pre-trained model available out there and the drastic time consumption difference amongst the above-mentioned models.
deep-learning computer-vision transfer-learning inception finetuning
asked Oct 4 '18 at 10:20
thanatozthanatoz
504317
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add a comment |
$begingroup$
I had been working with pre-trained models and was just curious to know the fastest forward propagating model of all the computer vision pre-trained models. I have been trying to achieve faster processing in one-shot learning and have tried the forward propagation with few models over a single image and the results are as follows:
VGG16: 4.857 seconds
ResNet50: 0.227 seconds
Inception: 0.135 seconds
Can you tell the fastest pre-trained model available out there and the drastic time consumption difference amongst the above-mentioned models.
deep-learning computer-vision transfer-learning inception finetuning
asked Oct 4 '18 at 10:20
thanatozthanatoz
504317
$endgroup$
add a comment |
$begingroup$
I had been working with pre-trained models and was just curious to know the fastest forward propagating model of all the computer vision pre-trained models. I have been trying to achieve faster processing in one-shot learning and have tried the forward propagation with few models over a single image and the results are as follows:
VGG16: 4.857 seconds
ResNet50: 0.227 seconds
Inception: 0.135 seconds
Can you tell the fastest pre-trained model available out there and the drastic time consumption difference amongst the above-mentioned models.
deep-learning computer-vision transfer-learning inception finetuning
asked Oct 4 '18 at 10:20
thanatozthanatoz
504317
$endgroup$
I had been working with pre-trained models and was just curious to know the fastest forward propagating model of all the computer vision pre-trained models. I have been trying to achieve faster processing in one-shot learning and have tried the forward propagation with few models over a single image and the results are as follows:
VGG16: 4.857 seconds
ResNet50: 0.227 seconds
Inception: 0.135 seconds
Can you tell the fastest pre-trained model available out there and the drastic time consumption difference amongst the above-mentioned models.
deep-learning computer-vision transfer-learning inception finetuning
deep-learning computer-vision transfer-learning inception finetuning
asked Oct 4 '18 at 10:20
thanatozthanatoz
504317
asked Oct 4 '18 at 10:20
thanatozthanatoz
504317
edited 13 mins ago
thanatoz
asked Oct 4 '18 at 10:20
thanatozthanatoz
504317
asked Oct 4 '18 at 10:20
thanatozthanatoz
504317
asked Oct 4 '18 at 10:20
thanatozthanatoz
504317
504317
add a comment |
add a comment |
1 Answer
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The answer will depend on some things such as your hardware and the image you process. Additional, we should distinguish if you are talking about a single run through the network in training mode or in inference mode. In the former, additional parameters are pre-computed and cached as well as several layers, such as dropout, being used, which are simply left out during inference. I will assume you want to simply produce a single prediction for a single image, so we are talking about inference time.
Factors
The basic correlation will be:
- more parameters (i.e. learnable weights, bigger network) - slower than a model with less parameters
- more recurrent units - slower than a convolutional network, which is slower than a full-connected network1
- complicated activation functions - slower than simple ones, such as ReLU
- deeper networks - slower than shallow networks (with same number of parameters) as less run in parallel on a GPU
Having listed a few factors in the final inference time required (time taken to produce one forward run through the network), I would guess that MobileNetV2 is probably among the fastest pre-trained model (available in Keras). We can see from the following table that this network has a small memory footprint of only 14 megabytes with ~3.5 million parameters. Compare that to your VGG test, with its ~138 million... 40 times more! In addition, the main workhorse layer of MobileNetV2 is a conv layer - they are essentially clever and smaller versions of residual networks.
Extra considerations
The reason I included the whole table above was to highlight that with small memory footprints and fast inference times, comes a cost: low accuracies!
If you compute the ratios of top-5 accuracy versus number of parameters (and generally versus memory), you might find a nice balance between inference time and performance.
1 Have a look at this comparison of CNNs with Recurrent modules
answered Oct 4 '18 at 22:31
n1k31t4n1k31t4
6,4362320
$endgroup$
$begingroup$
It is not as simple as looking at the number of parameters and depth. For instance, ResNet50 is significantly faster than Xception on my hardware, despite having more parameters and a higher depth. Similarly, MobileNet is faster than MovileNetV2 for me. That being said, the mobile nets are effectively built for embedded hardware, and thus less demanding.
$endgroup$
– Wok
Feb 8 at 13:02
$begingroup$
@Wok - You're right, which is why I didn't say that. There are many factors, I gave examples. You would need to do some benchmarking of models on target hardware, given certain data. In a very simplistic approach, the number of parameters is a proxy for number of operations, which is why I chose to include that table and also highlight correlations in memory consumption and model accuracy.
$endgroup$
– n1k31t4
Feb 8 at 13:55
add a comment |
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1 Answer
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1 Answer
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active
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votes
$begingroup$
The answer will depend on some things such as your hardware and the image you process. Additional, we should distinguish if you are talking about a single run through the network in training mode or in inference mode. In the former, additional parameters are pre-computed and cached as well as several layers, such as dropout, being used, which are simply left out during inference. I will assume you want to simply produce a single prediction for a single image, so we are talking about inference time.
Factors
The basic correlation will be:
- more parameters (i.e. learnable weights, bigger network) - slower than a model with less parameters
- more recurrent units - slower than a convolutional network, which is slower than a full-connected network1
- complicated activation functions - slower than simple ones, such as ReLU
- deeper networks - slower than shallow networks (with same number of parameters) as less run in parallel on a GPU
Having listed a few factors in the final inference time required (time taken to produce one forward run through the network), I would guess that MobileNetV2 is probably among the fastest pre-trained model (available in Keras). We can see from the following table that this network has a small memory footprint of only 14 megabytes with ~3.5 million parameters. Compare that to your VGG test, with its ~138 million... 40 times more! In addition, the main workhorse layer of MobileNetV2 is a conv layer - they are essentially clever and smaller versions of residual networks.
Extra considerations
The reason I included the whole table above was to highlight that with small memory footprints and fast inference times, comes a cost: low accuracies!
If you compute the ratios of top-5 accuracy versus number of parameters (and generally versus memory), you might find a nice balance between inference time and performance.
1 Have a look at this comparison of CNNs with Recurrent modules
answered Oct 4 '18 at 22:31
n1k31t4n1k31t4
6,4362320
$endgroup$
$begingroup$
It is not as simple as looking at the number of parameters and depth. For instance, ResNet50 is significantly faster than Xception on my hardware, despite having more parameters and a higher depth. Similarly, MobileNet is faster than MovileNetV2 for me. That being said, the mobile nets are effectively built for embedded hardware, and thus less demanding.
$endgroup$
– Wok
Feb 8 at 13:02
$begingroup$
@Wok - You're right, which is why I didn't say that. There are many factors, I gave examples. You would need to do some benchmarking of models on target hardware, given certain data. In a very simplistic approach, the number of parameters is a proxy for number of operations, which is why I chose to include that table and also highlight correlations in memory consumption and model accuracy.
$endgroup$
– n1k31t4
Feb 8 at 13:55
add a comment |
$begingroup$
The answer will depend on some things such as your hardware and the image you process. Additional, we should distinguish if you are talking about a single run through the network in training mode or in inference mode. In the former, additional parameters are pre-computed and cached as well as several layers, such as dropout, being used, which are simply left out during inference. I will assume you want to simply produce a single prediction for a single image, so we are talking about inference time.
Factors
The basic correlation will be:
- more parameters (i.e. learnable weights, bigger network) - slower than a model with less parameters
- more recurrent units - slower than a convolutional network, which is slower than a full-connected network1
- complicated activation functions - slower than simple ones, such as ReLU
- deeper networks - slower than shallow networks (with same number of parameters) as less run in parallel on a GPU
Having listed a few factors in the final inference time required (time taken to produce one forward run through the network), I would guess that MobileNetV2 is probably among the fastest pre-trained model (available in Keras). We can see from the following table that this network has a small memory footprint of only 14 megabytes with ~3.5 million parameters. Compare that to your VGG test, with its ~138 million... 40 times more! In addition, the main workhorse layer of MobileNetV2 is a conv layer - they are essentially clever and smaller versions of residual networks.
Extra considerations
The reason I included the whole table above was to highlight that with small memory footprints and fast inference times, comes a cost: low accuracies!
If you compute the ratios of top-5 accuracy versus number of parameters (and generally versus memory), you might find a nice balance between inference time and performance.
1 Have a look at this comparison of CNNs with Recurrent modules
answered Oct 4 '18 at 22:31
n1k31t4n1k31t4
6,4362320
$endgroup$
$begingroup$
It is not as simple as looking at the number of parameters and depth. For instance, ResNet50 is significantly faster than Xception on my hardware, despite having more parameters and a higher depth. Similarly, MobileNet is faster than MovileNetV2 for me. That being said, the mobile nets are effectively built for embedded hardware, and thus less demanding.
$endgroup$
– Wok
Feb 8 at 13:02
$begingroup$
@Wok - You're right, which is why I didn't say that. There are many factors, I gave examples. You would need to do some benchmarking of models on target hardware, given certain data. In a very simplistic approach, the number of parameters is a proxy for number of operations, which is why I chose to include that table and also highlight correlations in memory consumption and model accuracy.
$endgroup$
– n1k31t4
Feb 8 at 13:55
add a comment |
$begingroup$
The answer will depend on some things such as your hardware and the image you process. Additional, we should distinguish if you are talking about a single run through the network in training mode or in inference mode. In the former, additional parameters are pre-computed and cached as well as several layers, such as dropout, being used, which are simply left out during inference. I will assume you want to simply produce a single prediction for a single image, so we are talking about inference time.
Factors
The basic correlation will be:
- more parameters (i.e. learnable weights, bigger network) - slower than a model with less parameters
- more recurrent units - slower than a convolutional network, which is slower than a full-connected network1
- complicated activation functions - slower than simple ones, such as ReLU
- deeper networks - slower than shallow networks (with same number of parameters) as less run in parallel on a GPU
Having listed a few factors in the final inference time required (time taken to produce one forward run through the network), I would guess that MobileNetV2 is probably among the fastest pre-trained model (available in Keras). We can see from the following table that this network has a small memory footprint of only 14 megabytes with ~3.5 million parameters. Compare that to your VGG test, with its ~138 million... 40 times more! In addition, the main workhorse layer of MobileNetV2 is a conv layer - they are essentially clever and smaller versions of residual networks.
Extra considerations
The reason I included the whole table above was to highlight that with small memory footprints and fast inference times, comes a cost: low accuracies!
If you compute the ratios of top-5 accuracy versus number of parameters (and generally versus memory), you might find a nice balance between inference time and performance.
1 Have a look at this comparison of CNNs with Recurrent modules
answered Oct 4 '18 at 22:31
n1k31t4n1k31t4
6,4362320
$endgroup$
The answer will depend on some things such as your hardware and the image you process. Additional, we should distinguish if you are talking about a single run through the network in training mode or in inference mode. In the former, additional parameters are pre-computed and cached as well as several layers, such as dropout, being used, which are simply left out during inference. I will assume you want to simply produce a single prediction for a single image, so we are talking about inference time.
Factors
The basic correlation will be:
- more parameters (i.e. learnable weights, bigger network) - slower than a model with less parameters
- more recurrent units - slower than a convolutional network, which is slower than a full-connected network1
- complicated activation functions - slower than simple ones, such as ReLU
- deeper networks - slower than shallow networks (with same number of parameters) as less run in parallel on a GPU
Having listed a few factors in the final inference time required (time taken to produce one forward run through the network), I would guess that MobileNetV2 is probably among the fastest pre-trained model (available in Keras). We can see from the following table that this network has a small memory footprint of only 14 megabytes with ~3.5 million parameters. Compare that to your VGG test, with its ~138 million... 40 times more! In addition, the main workhorse layer of MobileNetV2 is a conv layer - they are essentially clever and smaller versions of residual networks.
Extra considerations
The reason I included the whole table above was to highlight that with small memory footprints and fast inference times, comes a cost: low accuracies!
If you compute the ratios of top-5 accuracy versus number of parameters (and generally versus memory), you might find a nice balance between inference time and performance.
1 Have a look at this comparison of CNNs with Recurrent modules
answered Oct 4 '18 at 22:31
n1k31t4n1k31t4
6,4362320
answered Oct 4 '18 at 22:31
n1k31t4n1k31t4
6,4362320
answered Oct 4 '18 at 22:31
n1k31t4n1k31t4
6,4362320
answered Oct 4 '18 at 22:31
n1k31t4n1k31t4
6,4362320
6,4362320
$begingroup$
It is not as simple as looking at the number of parameters and depth. For instance, ResNet50 is significantly faster than Xception on my hardware, despite having more parameters and a higher depth. Similarly, MobileNet is faster than MovileNetV2 for me. That being said, the mobile nets are effectively built for embedded hardware, and thus less demanding.
$endgroup$
– Wok
Feb 8 at 13:02
$begingroup$
@Wok - You're right, which is why I didn't say that. There are many factors, I gave examples. You would need to do some benchmarking of models on target hardware, given certain data. In a very simplistic approach, the number of parameters is a proxy for number of operations, which is why I chose to include that table and also highlight correlations in memory consumption and model accuracy.
$endgroup$
– n1k31t4
Feb 8 at 13:55
add a comment |
$begingroup$
It is not as simple as looking at the number of parameters and depth. For instance, ResNet50 is significantly faster than Xception on my hardware, despite having more parameters and a higher depth. Similarly, MobileNet is faster than MovileNetV2 for me. That being said, the mobile nets are effectively built for embedded hardware, and thus less demanding.
$endgroup$
– Wok
Feb 8 at 13:02
$begingroup$
@Wok - You're right, which is why I didn't say that. There are many factors, I gave examples. You would need to do some benchmarking of models on target hardware, given certain data. In a very simplistic approach, the number of parameters is a proxy for number of operations, which is why I chose to include that table and also highlight correlations in memory consumption and model accuracy.
$endgroup$
– n1k31t4
Feb 8 at 13:55
$begingroup$
It is not as simple as looking at the number of parameters and depth. For instance, ResNet50 is significantly faster than Xception on my hardware, despite having more parameters and a higher depth. Similarly, MobileNet is faster than MovileNetV2 for me. That being said, the mobile nets are effectively built for embedded hardware, and thus less demanding.
$endgroup$
– Wok
Feb 8 at 13:02
$begingroup$
It is not as simple as looking at the number of parameters and depth. For instance, ResNet50 is significantly faster than Xception on my hardware, despite having more parameters and a higher depth. Similarly, MobileNet is faster than MovileNetV2 for me. That being said, the mobile nets are effectively built for embedded hardware, and thus less demanding.
$endgroup$
– Wok
Feb 8 at 13:02
$begingroup$
@Wok - You're right, which is why I didn't say that. There are many factors, I gave examples. You would need to do some benchmarking of models on target hardware, given certain data. In a very simplistic approach, the number of parameters is a proxy for number of operations, which is why I chose to include that table and also highlight correlations in memory consumption and model accuracy.
$endgroup$
– n1k31t4
Feb 8 at 13:55
$begingroup$
@Wok - You're right, which is why I didn't say that. There are many factors, I gave examples. You would need to do some benchmarking of models on target hardware, given certain data. In a very simplistic approach, the number of parameters is a proxy for number of operations, which is why I chose to include that table and also highlight correlations in memory consumption and model accuracy.
$endgroup$
– n1k31t4
Feb 8 at 13:55
add a comment |
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