I decided to fast-forward the Planet dataset from Kaggle from planet competition, or the alternative name of this challenge was “understanding the amazon from space”. It touches the problem of wood cutting.

I used FastAi to examine things, specially I wanted to examine what kind of loss function will be used in this case of multi-label classification problem.

There was a general note ImageList class should be used for this set:

src = (ImageList.from_csv(path, 'train_v2.csv', folder='train-jpg', suffix='.jpg')
       .label_from_df(label_delim=' '))

My guess was that loss should not be inside ImageList class but inside some other class, probable set with:

self.crit or self.loss or self.loss_fn

And I found that pattern inside the class Learner:

class Learner():
    def __init__(self, data, models, opt_fn=None, tmp_name='tmp', models_name='models', metrics=None, clip=None, crit=None):
        self.data_,self.models,self.metrics,self.clip = data,models,metrics,clip
        self.wd_sched = None
        self.opt_fn = opt_fn or SGD_Momentum(0.9)
        self.tmp_path = tmp_name if os.path.isabs(tmp_name) else os.path.join(self.data.path, tmp_name)
        self.models_path = models_name if os.path.isabs(models_name) else os.path.join(self.data.path, models_name)
        os.makedirs(self.tmp_path, exist_ok=True)
        os.makedirs(self.models_path, exist_ok=True)
        self.crit = crit if crit else self._get_crit(data) # <---
        self.reg_fn = None
        self.fp16 = False

Also note, handy things we discovered:

  • we may set fp16 precision for the learning process.
  • default optimization function is SGD_Momentum(0.9) .

* Note momentum in here is the Nestorov momentum.

And so, the loss should not be anywhere before creating the learner object which is something we create using the cnn_learner class.

learn = cnn_learner(data, models.resnet50, metrics=[acc_02, f_score])

Once we have the learner object I got the feedback on loss function like this.



FlattenedLoss of BCEWithLogitsLoss()

This is nearly what I expected for this kind of problem so I set a little ☑.