The significance of multi-scale features has been gradually recognized by the edge detection community. However, the fusion of multi-scale features increases the complexity of the model, which is not friendly to practical application. In this work, we propose a Compact Twice Fusion Network (CTFN) to fully integrate multi-scale features while maintaining the compactness of the model. CTFN includes two lightweight multi-scale feature fusion modules: a Semantic Enhancement Module (SEM) that can utilize the semantic information contained in coarse-scale features to guide the learning of fine-scale features, and a Pseudo Pixel-level Weighting (PPW) module that aggregate the complementary merits of multi-scale features by assigning weights to all features. Notwithstanding all this, the interference of texture noise makes the correct classification of some pixels still a challenge. For these hard samples, we propose a novel loss function, coined Dynamic Focal Loss, which reshapes the standard cross-entropy loss and dynamically adjusts the weights to correct the distribution of hard samples. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and BIPEDv2. Compared with state-of-the-art methods, CTFN achieves competitive accuracy with less parameters and computational cost. Apart from the backbone, CTFN requires only 0.1M additional parameters, which reduces its computation cost to just 60% of other state-of-the-art methods. The codes are available at https://github.com/Li-yachuan/CTFN-pytorch-master.

The diagnostics plot for this particular model shows a decently good fit . When being used for prediction, it followed the real trend closely. And since our focus is on the estimates/coefficients of the bool_promotion variable, I considered this model good enough to be used in our analysis. As we can see from the model summary, our bool_promotion variable is significant, meaning it's showed to affect sales of grocery I at store 1, and in this case, positively. Having promotions added more than 500 units to the sales for this given combination. Having figured out the pipeline throughout these steps, I automated this process for other store-city-product combinations with auto_arima(), which helps us identify the best fit set of orders, record these orders, as well as coefficients. First, I created a helper function to identify the necessary parameters and train the auto_arima(). One parameter that appeared tricky to me was parameter m, which is the period for seasonal differencing.