# Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification
## 3 Results
### Object Detector Evaluation  
In Figure 2, the mean accuracy precision of all trained models using Pascal VOC metrics is presented. One can notice that the highest mAP is obtained for the SSD MobileNetV2 model pre-trained on the OID dataset. The SSD MobileNetV2 trained on the COCO database [40] is less accurate, although the architecture is similar to the one with best results and the parameter tuning is the same. This might happen because unlike the SSD MobileNetV2 model pre-trained on OID, the weights of the pre-trained model on COCO are frozen at the bottom layers, while the weights at the top layers, where the representative features are learned, are unfrozen in order to be updated according to the TrashNet dataset. The Inception-based SSD and Faster R-CNN model obtained mAP scores over 91%.  
In Figure 3, the recall of all trained models is evaluated. As in the case of mAP, the model with the best results in terms of recall is the SSD MobileNetV2 pre-trained on the COCO dataset, followed by Faster R-CNN and Inception V2.  
In order to check the models against overfitting, the training and evaluation losses during the entire training have been taken into account. In Figure 4, the classification loss of the most accurate model, the SSD MobileNetV2 network pre-trained on OID V4, is presented during training and evaluation. One can observe that the validation and training losses converge on the same graph, meaning that the CNN manages to generalize well and the images in the training set are accurately detected. The evaluation loss converges to a minimal value around 0.75 after 200 k steps. Due to the fact that the learning rate has been adjusted manually at some checkpoints, there are some sharp increases in the evaluation loss but returns to the prior variation when the changes are undone and meta-parameters are better adjusted. The training and evaluation errors decrease fast during the first part of training (50 k steps). After 50 k steps, the losses decrease on a smoother slope until 150 k steps. From that point on, the classification loss starts to converge until 220 k, when the model begins to overfit (the training loss fluctuates around 0.4, while the validation loss increases). The same evolution has been observed for other models. In order to avoid overfitting, the inference graph of the model has  
Figure 2: Mean average precision using Pascal VOC metrics.  
been generated from the checkpoint created at the 203 k step, when the network starts to lose its ability to generalize.  
In Figure 5, the localization loss of the SSD MobileNetV2 network pre-trained on OID V4 over time is presented. The validation and training losses follow the same pattern and the error converges after 100 k steps to an average value of 0.04 mm. Just like the classification loss, the same evolution can be observed for other models. The localization loss for training and validation is very small, thus it is not taken into account when evaluating the ability to generalize of the model.  
Figure 4: Classification loss training vs. validation MobileNetV2 pre-trained on OID.  
Figure 5: Localization loss training vs. validation MobileNetV2 pre-trained on OID.  
Figure 3: Recall of the trained models using Pascal VOC metrics.  
In Figure 6, the learning rates during training are presented. For two of the models, the training starts with a value of 0.004 and decreases until reaching 0.00001 (\(1\times 10^{-5}\)), switching from an exponential to a step decrease. The other two models start from a lower value of 0.0004 until reaching \(1\times 10^{-5}\).  
In Figure 7, the classification losses of the architectures used in this research are presented. The lowest classification loss comes from the Faster R-CNN network, while the highest is from the SSD Inception V2 model.