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===ResNet50===
The model, during the training phase, shows an increasing trend in accuracy over the train subset samples and over the validation subset samples. This is a sign that the model is learning in a correct way since it is not underfitting the train data. Furthermore, by looking at the trend of the loss during the 1000 training epochs, the model is clearly not overfitting the train data. By saving the status of the model with checkpoints each time there is an improvement in the validation loss, the best result is found at '''''epoch 993''''' with an '''''accuracy of 93.59%''''' and a '''''loss of 0.1912''''' on the validation data.
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===ResNet101===
The model, during the training phase, shows an increasing trend in accuracy over the train subset samples and over the validation subset samples. This is a sign that the model is learning in a correct way since it is not underfitting the train data. Furthermore, by looking at the trend of the loss during the 1000 training epochs, the model is clearly not overfitting the train data. By saving the status of the model with checkpoints each time there is an improvement in the validation loss, the best result is found at '''''epoch 944''''' with an '''''accuracy of 98.12%''''' and a '''''loss of 0.0781''''' on the validation data.
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To perform the inference over the images, only one DPU core is used for 1 thread, leading to almost a 70% utilization of the DPU-01 core. By increasing the number of threads i.e. with 4 threads, more cores are used and the percentage gets higher, very close to 100% on DPU-00 core and close to 95% on DPU-01 core. Concerning the DPU latency, for 1 thread the average latency for one image is about 21ms (21339.73μs). By increasing the concurrency, the latency for both cores is higher, about 24ms (24313.61μs) for DPU-00 core, and 22ms (22231.22μs) for DPU-01 core when using 2 threads and about 25ms (25385.51μs) for DPU-00 core, and 23ms (23025.89μs) for DPU-01 core with 4 concurrent threads.
===ResNet152===
The model, during the training phase, shows an increasing trend in accuracy over the train subset samples and over the validation subset samples. This is a sign that the model is learning in a correct way since it is not underfitting the train data. Furthermore, by looking at the trend of the loss during the 1000 training epochs, the model is clearly not overfitting the train data. By saving the status of the model with checkpoints each time there is an improvement in the validation loss, the best result is found at '''''epoch 969''''' with an '''''accuracy of 97.66%''''' and a '''''loss of 0.0721''''' on the validation data.
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To perform the inference over the images, only one DPU core is used for 1 thread, leading to almost a 80% utilization of the DPU-01 core. By increasing the number of threads i.e. with 4 threads, more cores are used and the percentage gets higher, very close to 100% on both DPU-00 and DPU-01 cores. Concerning the DPU latency, for 1 thread the average latency for one image is about 28ms (28867.86μs). By increasing the concurrency, the latency for both cores is higher, about 33ms 32702.59 for DPU-00 core, and 30ms 30046.64 for DPU-01 core when using 2 threads and about 34ms (33826.30μs) for DPU-00 core, and 30ms (30834.46μs) for DPU-01 core with 4 concurrent threads.
===InceptionV4===
The model, during the training phase, shows an increasing trend in accuracy over the train subset samples and over the validation subset samples. This is a sign that the model is learning in a correct way since it is not underfitting the train data. Furthermore, by looking at the trend of the loss during the 1000 training epochs, the model is clearly not overfitting the train data. By saving the status of the model with checkpoints each time there is an improvement in the validation loss, the best result is found at '''''epoch 957''''' with an '''''accuracy of 95.00%''''' and a '''''loss of 0.1729''''' on the validation data.
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===Inception ResNet V1===
The model, during the training phase, shows an increasing trend in accuracy over the train subset samples and over the validation subset samples. This is a sign that the model is learning in a correct way since it is not underfitting the train data. Furthermore, by looking at the trend of the loss during the 1000 training epochs, the model is clearly not overfitting the train data. By saving the status of the model with checkpoints each time there is an improvement in the validation loss, the best result is found at '''''epoch 959''''' with an '''''accuracy of 97.97%''''' and a '''''loss of 0.0751''''' on the validation data.
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===Inception ResNet V2===
The model, during the training phase, shows an increasing trend in accuracy over the train subset samples and over the validation subset samples. This is a sign that the model is learning in a correct way since it is not underfitting the train data. Furthermore, by looking at the trend of the loss during the 1000 training epochs, the model is clearly not overfitting the train data. By saving the status of the model with checkpoints each time there is an improvement in the validation loss, the best result is found at '''''epoch 974''''' with an '''''accuracy of 97.50%''''' and a '''''loss of 0.0724''''' on the validation data.
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