Is it reliable to use TensorFlow (ML in general) to classify baggage bag tags based on the presence of a green stripe? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsWhat is the right way to implement the graph of validation process of Batch Normalization in tensorflow?Fine-tuning a model from an existing checkpoint with TensorFlow-SlimWhy is my CNN model not learning anything? (tensorflow)RNN unable to classify time seriesHow to improve loss and avoid overfittingData augmentation based on the class type in the CNN modelTensorflow CNN sometimes converges, sometimes notCNN not learning properlyHow to increase accuracy of model from tensorflow model zoo?predict gives the same output value for every image (Keras)
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Is it reliable to use TensorFlow (ML in general) to classify baggage bag tags based on the presence of a green stripe?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhat is the right way to implement the graph of validation process of Batch Normalization in tensorflow?Fine-tuning a model from an existing checkpoint with TensorFlow-SlimWhy is my CNN model not learning anything? (tensorflow)RNN unable to classify time seriesHow to improve loss and avoid overfittingData augmentation based on the class type in the CNN modelTensorflow CNN sometimes converges, sometimes notCNN not learning properlyHow to increase accuracy of model from tensorflow model zoo?predict gives the same output value for every image (Keras)
$begingroup$
The images are identical except for the presence of the stripe on the side.
I am trying to use a classify the images into 2 classes: greenStripe, noGreenStripe.
I tried to use tensorflow retrain with a small dataset (~40 pictures in each class and batch size of 8) but the results where really bad. I am afraid to commiting to training using more data as it is time consuming.
What do you suggest? Is there a better approach or does the problem lie in the small training dataset?
classification tensorflow image-classification
asked Sep 18 '18 at 7:38
LonsomeHellLonsomeHell
62
$endgroup$
bumped to the homepage by Community♦ 26 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
The images are identical except for the presence of the stripe on the side.
I am trying to use a classify the images into 2 classes: greenStripe, noGreenStripe.
I tried to use tensorflow retrain with a small dataset (~40 pictures in each class and batch size of 8) but the results where really bad. I am afraid to commiting to training using more data as it is time consuming.
What do you suggest? Is there a better approach or does the problem lie in the small training dataset?
classification tensorflow image-classification
asked Sep 18 '18 at 7:38
LonsomeHellLonsomeHell
62
$endgroup$
bumped to the homepage by Community♦ 26 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
The images are identical except for the presence of the stripe on the side.
I am trying to use a classify the images into 2 classes: greenStripe, noGreenStripe.
I tried to use tensorflow retrain with a small dataset (~40 pictures in each class and batch size of 8) but the results where really bad. I am afraid to commiting to training using more data as it is time consuming.
What do you suggest? Is there a better approach or does the problem lie in the small training dataset?
classification tensorflow image-classification
asked Sep 18 '18 at 7:38
LonsomeHellLonsomeHell
62
$endgroup$
The images are identical except for the presence of the stripe on the side.
I am trying to use a classify the images into 2 classes: greenStripe, noGreenStripe.
I tried to use tensorflow retrain with a small dataset (~40 pictures in each class and batch size of 8) but the results where really bad. I am afraid to commiting to training using more data as it is time consuming.
What do you suggest? Is there a better approach or does the problem lie in the small training dataset?
classification tensorflow image-classification
classification tensorflow image-classification
asked Sep 18 '18 at 7:38
LonsomeHellLonsomeHell
62
asked Sep 18 '18 at 7:38
LonsomeHellLonsomeHell
62
asked Sep 18 '18 at 7:38
LonsomeHellLonsomeHell
62
asked Sep 18 '18 at 7:38
LonsomeHellLonsomeHell
62
asked Sep 18 '18 at 7:38
LonsomeHellLonsomeHell
62
62
bumped to the homepage by Community♦ 26 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 26 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
The scientific answer would be, it depends.
In case you are using any kind of Deep net, then 40 images is far too little.
It might be helpful to describe your problem setting a little bit more in depth.
Are the bags always in the same place, or do they need to be localized first? These kind of details could help other users in their recommendations.
As a first approach, before you try a deep net or any kind of ML I would try a simple baseline first. Do you know what the exact pixel value of your green stripe is? You could then simply check whether this colour is present at all. This is rather coarse, but I would see how far this gets you and it is good to see whether your ML methods can beat this simple baseline.
Subsequently you could also think of trying to localize the bagtags (in whatever way you like) then cropping it and checking for the presence of this green stripe.
answered Sep 18 '18 at 8:44
Felix van DoornFelix van Doorn
576
$endgroup$
$begingroup$
I am using tensorflow retrain.py (I suppose it uses a deep net). The bagtags are standard (same dimensions colors etc). The bagtags can be anywhere but we can take closeup pictures. For localizing the bagtags what strategy do you suggest : The bagtags are rectangles with a barcode in them.
$endgroup$
– LonsomeHell
Sep 18 '18 at 8:50
add a comment |
$begingroup$
1) Could you upload sample images maybe? It would be easier to decide.
2) Your dataset is very small, training anything significant from scratch will most certainly overfit the model. Take an existing model, that knows what a bag is (e.g. Mask R-CNN) and finetune it to your problem by changing the loss function and some architecture.
3) Actual framework should not matter: work with whichever you find convenient.
answered Oct 18 '18 at 14:54
AlexAlex
366313
$endgroup$
$begingroup$
This image has a bagtag with the green stripe around it. The plastic wrap is around it is irrelevant. The bagtags either have the green stripe or don't have it/ have it in different color.
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
$begingroup$
image
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
The scientific answer would be, it depends.
In case you are using any kind of Deep net, then 40 images is far too little.
It might be helpful to describe your problem setting a little bit more in depth.
Are the bags always in the same place, or do they need to be localized first? These kind of details could help other users in their recommendations.
As a first approach, before you try a deep net or any kind of ML I would try a simple baseline first. Do you know what the exact pixel value of your green stripe is? You could then simply check whether this colour is present at all. This is rather coarse, but I would see how far this gets you and it is good to see whether your ML methods can beat this simple baseline.
Subsequently you could also think of trying to localize the bagtags (in whatever way you like) then cropping it and checking for the presence of this green stripe.
answered Sep 18 '18 at 8:44
Felix van DoornFelix van Doorn
576
$endgroup$
$begingroup$
I am using tensorflow retrain.py (I suppose it uses a deep net). The bagtags are standard (same dimensions colors etc). The bagtags can be anywhere but we can take closeup pictures. For localizing the bagtags what strategy do you suggest : The bagtags are rectangles with a barcode in them.
$endgroup$
– LonsomeHell
Sep 18 '18 at 8:50
add a comment |
$begingroup$
The scientific answer would be, it depends.
In case you are using any kind of Deep net, then 40 images is far too little.
It might be helpful to describe your problem setting a little bit more in depth.
Are the bags always in the same place, or do they need to be localized first? These kind of details could help other users in their recommendations.
As a first approach, before you try a deep net or any kind of ML I would try a simple baseline first. Do you know what the exact pixel value of your green stripe is? You could then simply check whether this colour is present at all. This is rather coarse, but I would see how far this gets you and it is good to see whether your ML methods can beat this simple baseline.
Subsequently you could also think of trying to localize the bagtags (in whatever way you like) then cropping it and checking for the presence of this green stripe.
answered Sep 18 '18 at 8:44
Felix van DoornFelix van Doorn
576
$endgroup$
$begingroup$
I am using tensorflow retrain.py (I suppose it uses a deep net). The bagtags are standard (same dimensions colors etc). The bagtags can be anywhere but we can take closeup pictures. For localizing the bagtags what strategy do you suggest : The bagtags are rectangles with a barcode in them.
$endgroup$
– LonsomeHell
Sep 18 '18 at 8:50
add a comment |
$begingroup$
The scientific answer would be, it depends.
In case you are using any kind of Deep net, then 40 images is far too little.
It might be helpful to describe your problem setting a little bit more in depth.
Are the bags always in the same place, or do they need to be localized first? These kind of details could help other users in their recommendations.
As a first approach, before you try a deep net or any kind of ML I would try a simple baseline first. Do you know what the exact pixel value of your green stripe is? You could then simply check whether this colour is present at all. This is rather coarse, but I would see how far this gets you and it is good to see whether your ML methods can beat this simple baseline.
Subsequently you could also think of trying to localize the bagtags (in whatever way you like) then cropping it and checking for the presence of this green stripe.
answered Sep 18 '18 at 8:44
Felix van DoornFelix van Doorn
576
$endgroup$
The scientific answer would be, it depends.
In case you are using any kind of Deep net, then 40 images is far too little.
It might be helpful to describe your problem setting a little bit more in depth.
Are the bags always in the same place, or do they need to be localized first? These kind of details could help other users in their recommendations.
As a first approach, before you try a deep net or any kind of ML I would try a simple baseline first. Do you know what the exact pixel value of your green stripe is? You could then simply check whether this colour is present at all. This is rather coarse, but I would see how far this gets you and it is good to see whether your ML methods can beat this simple baseline.
Subsequently you could also think of trying to localize the bagtags (in whatever way you like) then cropping it and checking for the presence of this green stripe.
answered Sep 18 '18 at 8:44
Felix van DoornFelix van Doorn
576
answered Sep 18 '18 at 8:44
Felix van DoornFelix van Doorn
576
answered Sep 18 '18 at 8:44
Felix van DoornFelix van Doorn
576
answered Sep 18 '18 at 8:44
Felix van DoornFelix van Doorn
576
576
$begingroup$
I am using tensorflow retrain.py (I suppose it uses a deep net). The bagtags are standard (same dimensions colors etc). The bagtags can be anywhere but we can take closeup pictures. For localizing the bagtags what strategy do you suggest : The bagtags are rectangles with a barcode in them.
$endgroup$
– LonsomeHell
Sep 18 '18 at 8:50
add a comment |
$begingroup$
I am using tensorflow retrain.py (I suppose it uses a deep net). The bagtags are standard (same dimensions colors etc). The bagtags can be anywhere but we can take closeup pictures. For localizing the bagtags what strategy do you suggest : The bagtags are rectangles with a barcode in them.
$endgroup$
– LonsomeHell
Sep 18 '18 at 8:50
$begingroup$
I am using tensorflow retrain.py (I suppose it uses a deep net). The bagtags are standard (same dimensions colors etc). The bagtags can be anywhere but we can take closeup pictures. For localizing the bagtags what strategy do you suggest : The bagtags are rectangles with a barcode in them.
$endgroup$
– LonsomeHell
Sep 18 '18 at 8:50
$begingroup$
I am using tensorflow retrain.py (I suppose it uses a deep net). The bagtags are standard (same dimensions colors etc). The bagtags can be anywhere but we can take closeup pictures. For localizing the bagtags what strategy do you suggest : The bagtags are rectangles with a barcode in them.
$endgroup$
– LonsomeHell
Sep 18 '18 at 8:50
add a comment |
$begingroup$
1) Could you upload sample images maybe? It would be easier to decide.
2) Your dataset is very small, training anything significant from scratch will most certainly overfit the model. Take an existing model, that knows what a bag is (e.g. Mask R-CNN) and finetune it to your problem by changing the loss function and some architecture.
3) Actual framework should not matter: work with whichever you find convenient.
answered Oct 18 '18 at 14:54
AlexAlex
366313
$endgroup$
$begingroup$
This image has a bagtag with the green stripe around it. The plastic wrap is around it is irrelevant. The bagtags either have the green stripe or don't have it/ have it in different color.
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
$begingroup$
image
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
add a comment |
$begingroup$
1) Could you upload sample images maybe? It would be easier to decide.
2) Your dataset is very small, training anything significant from scratch will most certainly overfit the model. Take an existing model, that knows what a bag is (e.g. Mask R-CNN) and finetune it to your problem by changing the loss function and some architecture.
3) Actual framework should not matter: work with whichever you find convenient.
answered Oct 18 '18 at 14:54
AlexAlex
366313
$endgroup$
$begingroup$
This image has a bagtag with the green stripe around it. The plastic wrap is around it is irrelevant. The bagtags either have the green stripe or don't have it/ have it in different color.
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
$begingroup$
image
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
add a comment |
$begingroup$
1) Could you upload sample images maybe? It would be easier to decide.
2) Your dataset is very small, training anything significant from scratch will most certainly overfit the model. Take an existing model, that knows what a bag is (e.g. Mask R-CNN) and finetune it to your problem by changing the loss function and some architecture.
3) Actual framework should not matter: work with whichever you find convenient.
answered Oct 18 '18 at 14:54
AlexAlex
366313
$endgroup$
1) Could you upload sample images maybe? It would be easier to decide.
2) Your dataset is very small, training anything significant from scratch will most certainly overfit the model. Take an existing model, that knows what a bag is (e.g. Mask R-CNN) and finetune it to your problem by changing the loss function and some architecture.
3) Actual framework should not matter: work with whichever you find convenient.
answered Oct 18 '18 at 14:54
AlexAlex
366313
answered Oct 18 '18 at 14:54
AlexAlex
366313
answered Oct 18 '18 at 14:54
AlexAlex
366313
answered Oct 18 '18 at 14:54
AlexAlex
366313
366313
$begingroup$
This image has a bagtag with the green stripe around it. The plastic wrap is around it is irrelevant. The bagtags either have the green stripe or don't have it/ have it in different color.
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
$begingroup$
image
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
add a comment |
$begingroup$
This image has a bagtag with the green stripe around it. The plastic wrap is around it is irrelevant. The bagtags either have the green stripe or don't have it/ have it in different color.
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
$begingroup$
image
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
$begingroup$
This image has a bagtag with the green stripe around it. The plastic wrap is around it is irrelevant. The bagtags either have the green stripe or don't have it/ have it in different color.
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
$begingroup$
This image has a bagtag with the green stripe around it. The plastic wrap is around it is irrelevant. The bagtags either have the green stripe or don't have it/ have it in different color.
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
$begingroup$
image
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
$begingroup$
image
$endgroup$
– LonsomeHell
Oct 22 '18 at 8:36
add a comment |
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