Oh! I didn’t see your recent post. Yes I kinda had a little feeling about the model size too.
In terms of my NVIDIA panel, I have submitted on the post about the systems management interface(smi) and all the information. To my understanding, the graphics card that I have is a mobile GPU with a low memory buffer that cannot accept over-sized memory space from these training models. Understandable. Updating drivers didn’t really work as well.
However here is a small update on the model but still a query.
I tried by (a) reducing the resnet 50 to resnet 18 and…
(b) reducing the training and validation data
and the model actually starting training successfully in my local system on cuda.
Here are the changes that I did to the data.
after specifying the training and validation in this line on the code.
df_valid = df[:11471]
df_train = df[11472:].reset_index()
df_train = df_train.drop(columns = ['index'])

I further reduced it to 10,000 train image data and 7000 valid image data
df_train = df_train.head(10000)
df_valid = df_valid.head(-7000)

Now that the model is training in this scenario, here is the question. Is this image data considered ‘small’ for training? or should I add more?