Dataset Link: https://www.kaggle.com/datasets/tom99763/testtt
Kaggle Notebook: https://www.kaggle.com/code/shasan07/fer-pytorch
The images were transformed and normalized using torch, then we fed them to the pretrained DenseNet121 model for fine-Tuning. (Several other popular pre-trained models are also listed along with it).
A learning rate of 1e-4 was used for the AdamW optimizer, along with batch size of 8, and the training was conducted for 50 epochs. The best validation accuracy achieved by our model is 94.49%.
Training Summary:
Epoch [1/50] Train Loss: 1.1639 | Train Acc: 57.76% || Val Loss: 0.5370 | Val Acc: 82.76% Saved new best model with val_acc: 82.76% Epoch [2/50] Train Loss: 0.4200 | Train Acc: 85.83% || Val Loss: 0.3926 | Val Acc: 86.73% Saved new best model with val_acc: 86.73% Epoch [3/50] Train Loss: 0.2386 | Train Acc: 92.34% || Val Loss: 0.3453 | Val Acc: 89.59% Saved new best model with val_acc: 89.59% Epoch [4/50] Train Loss: 0.1351 | Train Acc: 95.89% || Val Loss: 0.3586 | Val Acc: 89.90% Saved new best model with val_acc: 89.90% Epoch [5/50] Train Loss: 0.1163 | Train Acc: 96.38% || Val Loss: 0.2585 | Val Acc: 91.73% Saved new best model with val_acc: 91.73% Epoch [6/50] Train Loss: 0.0898 | Train Acc: 97.14% || Val Loss: 0.4773 | Val Acc: 92.55% Saved new best model with val_acc: 92.55% Epoch [7/50] Train Loss: 0.0843 | Train Acc: 97.14% || Val Loss: 0.2090 | Val Acc: 93.67% Saved new best model with val_acc: 93.67% Epoch [8/50] Train Loss: 0.0682 | Train Acc: 97.88% || Val Loss: 0.3692 | Val Acc: 89.39% Epoch [9/50] Train Loss: 0.0634 | Train Acc: 98.03% || Val Loss: 0.3282 | Val Acc: 91.73% Epoch [10/50] Train Loss: 0.0483 | Train Acc: 98.67% || Val Loss: 0.2505 | Val Acc: 92.35% Epoch [11/50] Train Loss: 0.0515 | Train Acc: 98.37% || Val Loss: 0.3511 | Val Acc: 91.94% Epoch [12/50] Train Loss: 0.0399 | Train Acc: 98.88% || Val Loss: 0.4205 | Val Acc: 89.59% Epoch [13/50] Train Loss: 0.0360 | Train Acc: 99.00% || Val Loss: 0.2864 | Val Acc: 92.55% Epoch [14/50] Train Loss: 0.0369 | Train Acc: 98.88% || Val Loss: 0.3243 | Val Acc: 92.24% Epoch [15/50] Train Loss: 0.0299 | Train Acc: 99.16% || Val Loss: 0.4572 | Val Acc: 90.51% Epoch [16/50] Train Loss: 0.0427 | Train Acc: 98.88% || Val Loss: 0.3331 | Val Acc: 90.92% Epoch [17/50] Train Loss: 0.0337 | Train Acc: 99.13% || Val Loss: 0.3805 | Val Acc: 91.22% Epoch [18/50] Train Loss: 0.0354 | Train Acc: 99.03% || Val Loss: 0.4215 | Val Acc: 89.80% Epoch [19/50] Train Loss: 0.0449 | Train Acc: 98.70% || Val Loss: 0.2631 | Val Acc: 92.65% Epoch [20/50] Train Loss: 0.0225 | Train Acc: 99.26% || Val Loss: 0.2593 | Val Acc: 93.88% Saved new best model with val_acc: 93.88% Epoch [21/50] Train Loss: 0.0298 | Train Acc: 99.08% || Val Loss: 0.3229 | Val Acc: 92.65% Epoch [22/50] Train Loss: 0.0189 | Train Acc: 99.34% || Val Loss: 0.3232 | Val Acc: 92.96% Epoch [23/50] Train Loss: 0.0320 | Train Acc: 99.23% || Val Loss: 0.4447 | Val Acc: 92.76% Epoch [24/50] Train Loss: 0.0253 | Train Acc: 99.26% || Val Loss: 0.3163 | Val Acc: 92.35% Epoch [25/50] Train Loss: 0.0300 | Train Acc: 99.23% || Val Loss: 0.2847 | Val Acc: 93.57% Epoch [26/50] Train Loss: 0.0167 | Train Acc: 99.46% || Val Loss: 0.3133 | Val Acc: 92.96% Epoch [27/50] Train Loss: 0.0194 | Train Acc: 99.57% || Val Loss: 0.2613 | Val Acc: 93.78% Epoch [28/50] Train Loss: 0.0299 | Train Acc: 99.13% || Val Loss: 0.3292 | Val Acc: 93.06% Epoch [29/50] Train Loss: 0.0210 | Train Acc: 99.34% || Val Loss: 0.3586 | Val Acc: 93.06% Epoch [30/50] Train Loss: 0.0211 | Train Acc: 99.36% || Val Loss: 0.3061 | Val Acc: 93.78% Epoch [31/50] Train Loss: 0.0211 | Train Acc: 99.41% || Val Loss: 0.3750 | Val Acc: 92.76% Epoch [32/50] Train Loss: 0.0169 | Train Acc: 99.46% || Val Loss: 0.2669 | Val Acc: 93.98% Saved new best model with val_acc: 93.98% Epoch [33/50] Train Loss: 0.0238 | Train Acc: 99.11% || Val Loss: 0.3924 | Val Acc: 92.14% Epoch [34/50] Train Loss: 0.0265 | Train Acc: 99.11% || Val Loss: 0.3600 | Val Acc: 92.45% Epoch [35/50] Train Loss: 0.0269 | Train Acc: 99.16% || Val Loss: 0.2951 | Val Acc: 93.88% Epoch [36/50] Train Loss: 0.0089 | Train Acc: 99.77% || Val Loss: 0.2502 | Val Acc: 93.47% Epoch [37/50] Train Loss: 0.0158 | Train Acc: 99.59% || Val Loss: 0.3338 | Val Acc: 92.86% Epoch [38/50] Train Loss: 0.0248 | Train Acc: 99.46% || Val Loss: 0.3596 | Val Acc: 92.96% Epoch [39/50] Train Loss: 0.0290 | Train Acc: 99.23% || Val Loss: 0.3590 | Val Acc: 92.24% Epoch [40/50] Train Loss: 0.0287 | Train Acc: 99.16% || Val Loss: 0.2972 | Val Acc: 93.78% Epoch [41/50] Train Loss: 0.0138 | Train Acc: 99.59% || Val Loss: 0.3068 | Val Acc: 92.76% Epoch [42/50] Train Loss: 0.0257 | Train Acc: 99.39% || Val Loss: 0.2569 | Val Acc: 93.57% Epoch [43/50] Train Loss: 0.0149 | Train Acc: 99.67% || Val Loss: 0.2555 | Val Acc: 94.18% Saved new best model with val_acc: 94.18% Epoch [44/50] Train Loss: 0.0145 | Train Acc: 99.49% || Val Loss: 0.3501 | Val Acc: 92.24% Epoch [45/50] Train Loss: 0.0093 | Train Acc: 99.74% || Val Loss: 0.3772 | Val Acc: 93.06% Epoch [46/50] Train Loss: 0.0210 | Train Acc: 99.49% || Val Loss: 0.3676 | Val Acc: 92.76% Epoch [47/50] Train Loss: 0.0131 | Train Acc: 99.74% || Val Loss: 0.2568 | Val Acc: 94.49% Saved new best model with val_acc: 94.49% Epoch [48/50] Train Loss: 0.0081 | Train Acc: 99.72% || Val Loss: 0.3309 | Val Acc: 93.88% Epoch [49/50] Train Loss: 0.0169 | Train Acc: 99.57% || Val Loss: 0.2729 | Val Acc: 92.96% Epoch [50/50] Train Loss: 0.0118 | Train Acc: 99.67% || Val Loss: 0.3195 | Val Acc: 93.57% Obtained Results:
Classification Report: precision recall f1-score support Anger 0.98 0.91 0.95 140 Disgust 0.90 0.97 0.93 140 Fear 0.93 0.89 0.91 140 Happy 0.99 0.99 0.99 140 Neutral 0.96 0.98 0.97 140 Sad 0.93 0.92 0.92 140 Surprise 0.93 0.96 0.94 140 accuracy 0.94 980 macro avg 0.95 0.94 0.94 980 weighted avg 0.95 0.94 0.94 980 Confusion Matrix: [[128 6 2 0 1 3 0] [ 1 136 0 1 0 2 0] [ 1 0 124 1 3 3 8] [ 0 0 1 138 0 0 1] [ 0 1 0 0 137 2 0] [ 0 7 2 0 1 129 1] [ 0 1 5 0 0 0 134]] 
