Open Kulbear opened 5 years ago
Training config:
def cos_annealing_lr(initial_lr, cur_epoch, epoch_per_cycle):
return initial_lr * (np.cos(np.pi * cur_epoch / epoch_per_cycle) + 1) / 2
Model used (with best score):
Ideas used:
c_value = country_mapping.get(df.iloc[i]['countrycode'], 40)
x[i, :, :, 0] = img + mask * c_value
if i == 0 or i == len(stroke[0]) - 2:
color = 255
_ = cv2.line(img, (stroke[0][i], stroke[1][i]),
(stroke[0][i + 1], stroke[1][i + 1]), color, lw)
def forward(self, x):
x = self.features(x)
x = self.relu(x)
x = self.avg_pool(x)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.last_linear(x)
Possible Models:
Size 1:
Size 2:
TODO:
https://www.kaggle.com/c/human-protein-atlas-image-classification/discussion/74374#437548
train_df_orig=train_df.copy()
lows = [15,15,15,8,9,10,8,9,10,8,9,10,17,20,24,26,15,27,15,20,24,17,8,15,27,27,27]
for i in lows:
target = str(i)
indicies = train_df_orig.loc[train_df_orig['Target'] == target].index
train_df = pd.concat([train_df,train_df_orig.loc[indicies]], ignore_index=True)
indicies = train_df_orig.loc[train_df_orig['Target'].str.startswith(target+" ")].index
train_df = pd.concat([train_df,train_df_orig.loc[indicies]], ignore_index=True)
indicies = train_df_orig.loc[train_df_orig['Target'].str.endswith(" "+target)].index
train_df = pd.concat([train_df,train_df_orig.loc[indicies]], ignore_index=True)
indicies = train_df_orig.loc[train_df_orig['Target'].str.contains(" "+target+" ")].index
train_df = pd.concat([train_df,train_df_orig.loc[indicies]], ignore_index=True)