Share your work here ✅

[zerotosingularity (Jan Van de Poel)]

Nice work. In relation to your open questions:

@wcneill

How many images per class is enough for your model to be robust when faced with new images? I read 1,000 per class in several places online. In the video Jeremy says something to the effect of “less than you would think”. After cleaning my dataset, I had anywhere between 25 and 50 good images per class.

You can get good results with limited images, but it will depend on how similar, different your objects are. In deep learning the more images the better (accuracy increases with more ‘interesting/good/different’ iamges). It’s important to have a representative dataset (training and validation), that represents the input data your model will see in production…

So you can get good results with 20-50 images, and start adding more of the images your model predicts when deployed to retrain your model…

@wcneill

1. After cleaning the data, why keep the parameters from the run on uncleaned data? This is what ended up working for me:

• Create combined DataBunch, no splits
• Train a model and get its top losses
• Use top losses to clean up noisy photos
• use ‘cleaned.csv’ to create a brand new DataBunch with appropriate train/valid/test split
• Start new model with new, clean DataBunch that it has never seen before.

Did you also check duplicates? They might end up being in both your training and validation set, skewing your results…

But indeed, no real need to keep those weights. Another cool trick to improve your model:

@zerotosingularity

data_224 (size 224) 

create network with data_224 

train network save network ("some_name_224") 

data_299 (size 299) 

create network 

with data_299 load saved network "some_name_224" 

train network

@wcneill

After enough epochs, I noticed that sometimes training and validation loss continue to drop while error_rate begins to creep up. From my understanding of loss, I thought it was directly tied to error rate. For example in linear regression, I thought loss and mean squared error are actually the same thing, and gradient descent is used to minimize loss, and thereby also minimizing error. So, it appears that I have made a conceptual error. What am I missing?

A first hunch, percentage of total errors goes up, but for the ones it predicts correctly, the loss is very low…

If you need help deploying your model, you might want to check out our Quick Guide for SeeMe.ai: https://github.com/zerotosingularity/seeme-quick-guides/blob/master/seeme-quick-guide-fastai-v1.ipynb