## 4 Experiments
### Transfer Learning and Bias Evaluation  
Models with shape bias are expected to exhibit enhanced accuracy and training efficiency for shape-dominant data. Thus, to evaluate the shape bias of the model, its accuracy was compared to shape-dominant data. The employed datasets encompassed logo [51], cartoon, sketch [52], and silhouette images. The silhouette datasets used were MPEG7 [53] and Animal2000 [54], and are considered relatively small in scale. The logo dataset comprised a collection of images featuring logos from various companies and products. The cartoon dataset consisted of images that capture cropped faces of characters from anime and similar media. The sketch dataset included line drawings of various objects, such as airplanes and apples, created in a short time. The MPEG7 dataset contained silhouette images of diverse objects, including apples and butterflies, while the Animal2000 dataset specifically focused on the collection of animal silhouette images. The number of images included in each dataset is presented in Table 3. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Architecture** & **ImageNet Val Accuracy (\%)** & **Parameters** \\ \hline Original & **77.92** & 25,028,904 \\ Kernel size: 5 & 75.80 & 27,543,848 \\ Kernel size: 7 & 75.75 & 31,316,264 \\ Dilation: 2 & 70.58 & 25,028,904 \\ Dilation: 3 & 69.03 & 25,028,904 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results represent the accuracy of the ImageNet validation set after pre-training for each architecture, along with the corresponding number of parameters. ImageNet consists of natural images, and the results reflect performance on a texture-biased image set. The bold text in the table indicates the highest accuracy, while the underlined text represents the second-highest accuracy. The accuracy is highest for the original and decreases as the architecture changes more. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Train** & **Test** & **Sum** \\ \hline Cartoon & 11,592 & 2898 & 14,490 \\ Sketch & 16,000 & 4000 & 20,000 \\ Logo & 116,958 & 50,182 & 167,140 \\ Animal2000 & 1800 & 200 & 2000 \\ MPEG7 & 1278 & 122 & 1400 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of data in each dataset was analyzed. Additionally, experiments reducing the data volume were conducted for the cartoon, sketch, and logo datasets. Classification and evaluation were performed on shape-dominant data or images with little local texture. Logo images often feature distinctive shapes, whereas sketch images and silhouette images solely comprise object contours, devoid of color or local textures. Additionally, compared with natural images, cartoon images contain fewer local textures, with identical objects often depicted in the same color. The classification and evaluation were performed using shape-dominant data or images with minimal local textures. In transfer learning, only the weights of the last classification layer are updated, while those of the CNN-based feature extractors are fixed. This allows evaluation of the effect of the feature-extractor bias on the classification accuracy. We also evaluated the accuracy after one and two epochs to compare the learning efficiency and demonstrate the impact of shape bias on the learning speed. In transfer learning, the learning rate was set to 0.01, and a cosine scheduler was used. Additionally, the batch size was set to 256, and the optimizer used was SGD. The model was trained for 40 epochs. Additionally, the proposed bias-evaluation metric was compared with those employed in previous studies. Conventional shape-bias metrics employ style transfer to stylistically transform images, generating images with two disparate labels for the global shape and local texture (e.g., the global shape of a car and the local texture of a cat). Additionally, they used 16 classes: airplanes, bears, bicycles, birds, boats, bottles, cars, cats, chairs, clocks, dogs, elephants, keyboards, knives, ovens, and trucks.