## 4 Experiments
### Results  
The results section showcases the experimental evaluations of the performance of our proposed methodology. In the subsequent part, we will provide a comprehensive analysis and discussion of these results. Our evaluation process involved assessing the precision, recall, accuracy, and F1-score for all categories within the video dataset. The effectiveness of video classification relies on the learning capabilities of the network and the specific models and cloth dataset utilized. To compare classification performance, we conducted experiments using three models: GRU, LSTM, and Transformer, on various video datasets. All models were trained for 250 epochs with a training sample of 400 videos. The transformer model was found to outperform the other mentioned models, as it achieved the highest accuracy. Specifically, the transformer achieved an impressively high accuracy of over 99.01% with 1275 video datasets, as show in Table 5. For training, our models were pre-trained on ResNet50. Unless otherwise specified, we utilized 32-image input clips to fine-tune our models. These clips were generated by randomly selecting 64 consecutive  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Software/Parameters** & **Value** \\ \hline Windows 10 & 64-bit \\ Programming Language & Python, Keras, TensorFlow \\ CPU & AMD Ryzen 9 5900x \\ GPU & NVIDIA GeForce RTX 4090 \\ RAM & 128 GB \\ Batch size & 32 \\ Validation split & 0.16 \\ Optimizer & Adam \\ Learning rate & 0.0001 \\ Loss function & sparse categorical cross-entropy \\ Epochs & 250 \\ Dataset & 1275 videos \\ \hline \hline \end{tabular}
\end{table}
Table 4: Software and Simulation Parameters.  
frames from the original full-length video and excluding frames with a spatial size of 224 \(\times\) 224 pixels.  
Regarding the training and evaluation of our proposed method, the image dataset was fed to the training classifier in batches of 32 images, and we parameterized the generator to apply a set of transformations such as flipping, rotating, and shifting in both dimensions to the input images. We partitioned the dataset into 70% for training and 30% for testing, with an additional 16% set aside as a validation dataset within the training data. The training process achieved a robust accuracy of 99.01% after 250 epochs, as shown in Figure 9. Figures 10-12 show the training history of the model.  
Table 6 presents the results of the classification performance of accuracy, precision, recall, and f1-score of the three methods. The results show that the transformer model achieved 99.01% accuracy, which was better than any of the other methods, and the precision, recall, and f1-score were higher than those of the LSTM and GRU models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Type** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Cloth with collision & 0.99 & 0.99 & 0.99 & 0.99 \\ Cloth falling dropdown & 0.97 & 0.97 & 0.97 & 0.97 \\ Cloth affected by airflow & 0.97 & 0.98 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of classification performance for three different types of cloths using transfer model: cloth with collision, cloth falling down, and cloth affected by airflow.  
Figure 9: Confusion Matrix from our experiment dataset on cloth with collision.  
Figure 11: Proposed model training history from cloth with dropdown: (**a**) Model accuracy; (**b**) Model loss.  
Figure 12: Proposed model training history from cloth affected by airflow: (**a**) Model accuracy; (**b**) Model loss.  
Figure 10: Proposed model training history from cloth with collision: (**a**) Model accuracy; (**b**) Model loss.