# A Closer Look at Few-Shot Classification with Many Novel Classes
## 5 Experiments
### FSL-MNC Performance  
This experiment included a detailed evaluation of the few-shot generalization capabilities of SHA-Pipeline in FSL-MNC scenarios. In this experiment, SHA-Pipeline utilized a fixed number of categories (\(N=5\)) for ProtoNet's meta-traininging to ensure training consistency and reduce the model's sensitivity to the number of categories. This approach aimed to test the model's learning efficiency and generalization capabilities under conditions with fewer categories. The core architecture for all methods employed the DINO pre-trained ViT-small backbone network. The choice of the DINO pre-trained model was due to its exceptional ability in handling image feature extraction, particularly in image understanding and representation learning. This high-performance backbone network provided SHA-Pipeline with robust visual information processing capabilities, making it more suitable for complex few-shot learning tasks. Table 2 displays the average accuracy results obtained through different methods. These results not only showcase the performance of SHA-Pipeline in FSL-MNC scenarios  
Figure 8: Memory cost of SHA-Pipeline during meta-testing. Note that the memory cost of ProtoNet increases when \(N>80\). but also compare the performance of other methods under the same conditions, offering a comprehensive performance assessment. This comparison clearly illustrates SHA-Pipeline's advantages in generalization capability and adaptation to new categories, as well as its ability to maintain stable performance in FSL-MNC environments. Additionally, these results validate the effectiveness of the fixed category number meta-traininging strategy in practical applications, providing important insights and guidance for future few-shot learning research. From Table 2, it is observed that the SHA-Pipeline method achieved the highest average accuracy across various category configurations. Particularly, in the setting with one sample per class, SHA-Pipeline's average accuracy reached 49.89%, which is 1.28% higher than its closest competitor, \(\text{P}>\text{M}>\text{F}\). This indicates that under more stringent few-shot conditions, SHA-Pipeline more effectively utilizes its category hierarchy fine-tuning mechanism to improve classification performance. In the experimental setting with five samples per class, SHA-Pipeline's average accuracy even reached 68.24%, which is 1.16% higher than \(\text{P}>\text{M}>\text{F}\), demonstrating its excellent performance when handling slightly more sample data. This performance enhancement is particularly noticeable as the number of categories increases, confirming that SHA-Pipeline is not only adaptable but also maintains stable performance under high-pressure FSL-MNC scenarios. Moreover, comparing results from different methods, it can be seen that SHA-Pipeline's gap with other methods becomes more pronounced in settings with more categories (such as 5-way and 10-way), possibly due to its unique category hierarchy and fine-tuning strategy being most effective when category information is more concentrated. Even when category numbers reach a massive scale (like 2560-way), although performance declines for all methods, the decline is relatively smaller for SHA-Pipeline, further proving its superior generalization capabilities and adaptability to complex classification environments. Overall, these data not only demonstrate the advantages of SHA-Pipeline in FSL-MNC scenarios but also highlight its ability to maintain consistent performance across different sample densities. This stability and excellence in performance provide significant experimental evidence and theoretical support for the field of few-shot learning, aiding in further development and application of related technologies. Figure 9 further illustrates the average accuracy trends across all methods in settings with 1 and 5 samples per class, as category numbers change.