No problem!
Hey i know its silly…
But how do you divide the train folder into valid (i mean getting everything perfectly into sub-folders).
I did it manually
200 images per category for training
the rest for validation.
1 Like
@gerardo
hmmm I would love to know if there’s any shortcut script if someone wrote it…
else I know what to do next 
1 Like
I created a CSV using python and then used from_csv.
3 Likes
I also wrote a script to create a labels.csv file with headers file,species.
from glob2 import glob
import pandas as pd
df = pd.DataFrame(columns=["file", "species"])
for image in glob("train/**/*.png"):
dir_ = image.split('/')
file_, species = dir_[-1], dir_[-2]
df = df.append({
"file": file_,
"species": species
}, ignore_index=True)
df.to_csv('labels.csv', index=False)
Then, you can use the from_csv method.
Once you are done creating labels.csv, don’t forget to remove the species folders in train. Keep the images, remove the folders.
17 Likes
Wow you killed it! What architecture are you using, if I may ask?
2 Likes
Resnet50…how about you?
Also resnet50. Oddly enough I found Resnext50 and Nasnet both a little worse (Resnext50 was better on validation, but worse when submitted).
I tried resnext50 first cause I also thought it would do better, but kept getting cuda out of memory errors on fine-tuning so thats how I ended up resorting to resnet50…will test out a few others. Also still need to train rn50 with all the data or cv, this is just one fold for now
Wow that’s impressive - mine is trained with all data.
To train the bigger models I decreased the batch size. For Nasnet I need bs=12 on a 16GB P3!
1 Like
Hey how did you include metrics in?
This is what i tried to do.
from sklearn.metrics import f1_score
def meanfscore(y_pred, targ):
return f1_score(targ, y_pred, average='micro')
learn.fit(0.01, 5, metrics=[meanfscore])
This should work but it’s throwing a weird error.
ValueError: Classification metrics can't handle a mix of multilabel-indicator and continuous-multioutput targets
Personally, I didn’t use f1_score as a metric during training. I just used regular accuracy. The LB is scored by f1 based on the predictions you submit.
2 Likes
log_preds,y = learn.TTA()
preds = np.exp(log_preds)
preds = np.argmax(log_preds, axis=1)
metrics.f1_score(data.val_y, preds, average=‘micro’)
I used this code to calculate the f1_score but I would like to add it the learn.fit but does not seems to be working.
I cannot believe it 10th place 
I have been using Kaggle for 2 years and this is the first time that I can accomplish this
Thanks @jeremy
14 Likes
Yup Can’t believe it either Thanks @jeremy and @jamesrequa
So top 4 is officially fastai students and community.
This I didn’t even used the whole training set…I think i can still get a bump using Kstratified CV
and using my full training set.
4 Likes
Hi,
Can someone share the accuracy / top 1 error rate for their model?
Regards
Here’s mine:
best validation loss 0.08718
best accuracy 0.97656
5 Likes