TESTING Results for VOC dataset ## TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: max ##
test_loss : 0.23001
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5529999732971191
Mean_Dice : 0.6190000176429749
Class_IoU : {0: 0.867, 1: 0.584, 2: 0.272, 3: 0.52, 4: 0.347, 5: 0.292, 6: 0.58, 7: 0.449, 8: 0.63, 9: 0.122, 10: 0.498, 11: 0.213, 12: 0.546, 13: 0.495, 14: 0.484, 15: 0.549, 16: 0.196, 17: 0.476, 18: 0.245, 19: 0.596, 20: 0.396}
Class_Dice : {0: 0.916, 1: 0.658, 2: 0.382, 3: 0.585, 4: 0.415, 5: 0.344, 6: 0.619, 7: 0.5, 8: 0.677, 9: 0.17, 10: 0.55, 11: 0.251, 12: 0.604, 13: 0.561, 14: 0.553, 15: 0.627, 16: 0.257, 17: 0.534, 18: 0.288, 19: 0.646, 20: 0.454}
## TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: T ##
test_loss : 0.23001
Pixel_Accuracy : 0.9919999837875366
Mean_IoU : 0.5669999718666077
Mean_Dice : 0.6359999775886536
Class_IoU : {0: 0.868, 1: 0.628, 2: 0.294, 3: 0.576, 4: 0.368, 5: 0.314, 6: 0.608, 7: 0.474, 8: 0.657, 9: 0.129, 10: 0.56, 11: 0.226, 12: 0.605, 13: 0.542, 14: 0.538, 15: 0.571, 16: 0.2, 17: 0.488, 18: 0.261, 19: 0.617, 20: 0.399}
Class_Dice : {0: 0.917, 1: 0.708, 2: 0.414, 3: 0.648, 4: 0.441, 5: 0.369, 6: 0.649, 7: 0.528, 8: 0.706, 9: 0.18, 10: 0.619, 11: 0.267, 12: 0.669, 13: 0.614, 14: 0.614, 15: 0.65, 16: 0.262, 17: 0.548, 18: 0.306, 19: 0.669, 20: 0.457}
## TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: rankdice ##
test_loss : 0.23001
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5709999799728394
Mean_Dice : 0.640999972820282
Class_IoU : {0: 0.868, 1: 0.63, 2: 0.301, 3: 0.574, 4: 0.375, 5: 0.321, 6: 0.612, 7: 0.482, 8: 0.661, 9: 0.144, 10: 0.565, 11: 0.23, 12: 0.608, 13: 0.546, 14: 0.541, 15: 0.571, 16: 0.216, 17: 0.497, 18: 0.269, 19: 0.621, 20: 0.404}
Class_Dice : {0: 0.917, 1: 0.71, 2: 0.422, 3: 0.647, 4: 0.449, 5: 0.377, 6: 0.654, 7: 0.538, 8: 0.71, 9: 0.2, 10: 0.624, 11: 0.272, 12: 0.672, 13: 0.618, 14: 0.618, 15: 0.652, 16: 0.283, 17: 0.557, 18: 0.315, 19: 0.674, 20: 0.462}
## TESTING Restuls for Model: DeepLab + Loss: FocalLoss + predict: max ##
test_loss : 0.09723
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5320000052452087
Mean_Dice : 0.6050000190734863
Class_IoU : {0: 0.862, 1: 0.638, 2: 0.256, 3: 0.629, 4: 0.395, 5: 0.322, 6: 0.63, 7: 0.437, 8: 0.582, 9: 0.119, 10: 0.455, 11: 0.262, 12: 0.538, 13: 0.472, 14: 0.444, 15: 0.513, 16: 0.185, 17: 0.474, 18: 0.238, 19: 0.623, 20: 0.392}
Class_Dice : {0: 0.913, 1: 0.725, 2: 0.369, 3: 0.713, 4: 0.481, 5: 0.383, 6: 0.679, 7: 0.493, 8: 0.636, 9: 0.168, 10: 0.512, 11: 0.309, 12: 0.603, 13: 0.544, 14: 0.511, 15: 0.596, 16: 0.241, 17: 0.543, 18: 0.279, 19: 0.684, 20: 0.453}
## TESTING Restuls for Model: DeepLab + Loss: FocalLoss + predict: T ##
test_loss : 0.09723
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5509999990463257
Mean_Dice : 0.6269999742507935
Class_IoU : {0: 0.862, 1: 0.647, 2: 0.258, 3: 0.651, 4: 0.416, 5: 0.329, 6: 0.637, 7: 0.462, 8: 0.652, 9: 0.124, 10: 0.482, 11: 0.263, 12: 0.605, 13: 0.528, 14: 0.517, 15: 0.547, 16: 0.186, 17: 0.551, 18: 0.257, 19: 0.669, 20: 0.401}
Class_Dice : {0: 0.913, 1: 0.737, 2: 0.374, 3: 0.737, 4: 0.506, 5: 0.39, 6: 0.685, 7: 0.521, 8: 0.71, 9: 0.176, 10: 0.542, 11: 0.311, 12: 0.68, 13: 0.608, 14: 0.595, 15: 0.631, 16: 0.242, 17: 0.631, 18: 0.302, 19: 0.735, 20: 0.465}
## TESTING Restuls for Model: DeepLab + Loss: FocalLoss + predict: rankdice ##
test_loss : 0.09723
Pixel_Accuracy : 0.9900000095367432
Mean_IoU : 0.5509999990463257
Mean_Dice : 0.6290000081062317
Class_IoU : {0: 0.867, 1: 0.632, 2: 0.253, 3: 0.64, 4: 0.398, 5: 0.344, 6: 0.647, 7: 0.469, 8: 0.656, 9: 0.154, 10: 0.487, 11: 0.278, 12: 0.607, 13: 0.525, 14: 0.515, 15: 0.543, 16: 0.191, 17: 0.556, 18: 0.274, 19: 0.677, 20: 0.403}
Class_Dice : {0: 0.917, 1: 0.726, 2: 0.373, 3: 0.732, 4: 0.493, 5: 0.408, 6: 0.697, 7: 0.531, 8: 0.716, 9: 0.213, 10: 0.55, 11: 0.327, 12: 0.682, 13: 0.609, 14: 0.597, 15: 0.631, 16: 0.254, 17: 0.641, 18: 0.319, 19: 0.744, 20: 0.469}
## TESTING Restuls for Model: DeepLab + Loss: BCEWithLogitsLoss2d + predict: T ##
test_loss : 0.02362
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.31700000166893005
Mean_Dice : 0.6330000162124634
Class_IoU : {0: 0.453, 1: 0.381, 2: 0.122, 3: 0.377, 4: 0.297, 5: 0.201, 6: 0.334, 7: 0.262, 8: 0.359, 9: 0.075, 10: 0.311, 11: 0.172, 12: 0.325, 13: 0.264, 14: 0.275, 15: 0.317, 16: 0.129, 17: 0.312, 18: 0.161, 19: 0.355, 20: 0.255}
Class_Dice : {0: 0.907, 1: 0.761, 2: 0.244, 3: 0.754, 4: 0.594, 5: 0.402, 6: 0.668, 7: 0.525, 8: 0.717, 9: 0.149, 10: 0.622, 11: 0.343, 12: 0.651, 13: 0.529, 14: 0.55, 15: 0.634, 16: 0.259, 17: 0.623, 18: 0.323, 19: 0.71, 20: 0.509}
## TESTING Restuls for Model: DeepLab + Loss: BCEWithLogitsLoss2d + predict: max ##
test_loss : 0.02362
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.29899999499320984
Mean_Dice : 0.5989999771118164
Class_IoU : {0: 0.455, 1: 0.343, 2: 0.16, 3: 0.365, 4: 0.255, 5: 0.188, 6: 0.293, 7: 0.231, 8: 0.329, 9: 0.071, 10: 0.279, 11: 0.132, 12: 0.269, 13: 0.25, 14: 0.209, 15: 0.291, 16: 0.123, 17: 0.292, 18: 0.134, 19: 0.311, 20: 0.238}
Class_Dice : {0: 0.909, 1: 0.685, 2: 0.32, 3: 0.73, 4: 0.509, 5: 0.376, 6: 0.586, 7: 0.462, 8: 0.659, 9: 0.142, 10: 0.559, 11: 0.264, 12: 0.538, 13: 0.501, 14: 0.418, 15: 0.582, 16: 0.246, 17: 0.583, 18: 0.268, 19: 0.622, 20: 0.477}
## TESTING Restuls for Model: DeepLab + Loss: BCEWithLogitsLoss2d + predict: rankdice ##
test_loss : 0.02362
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.3230000138282776
Mean_Dice : 0.6460000276565552
Class_IoU : {0: 0.455, 1: 0.382, 2: 0.15, 3: 0.383, 4: 0.307, 5: 0.209, 6: 0.341, 7: 0.27, 8: 0.362, 9: 0.093, 10: 0.326, 11: 0.183, 12: 0.33, 13: 0.27, 14: 0.276, 15: 0.323, 16: 0.141, 17: 0.321, 18: 0.173, 19: 0.36, 20: 0.257}
Class_Dice : {0: 0.91, 1: 0.764, 2: 0.299, 3: 0.767, 4: 0.613, 5: 0.418, 6: 0.682, 7: 0.541, 8: 0.724, 9: 0.187, 10: 0.652, 11: 0.366, 12: 0.661, 13: 0.541, 14: 0.552, 15: 0.646, 16: 0.281, 17: 0.643, 18: 0.346, 19: 0.719, 20: 0.515}
## TESTING Restuls for Model: DeepLab + Loss: LovaszSoftmax + predict: max ##
test_loss : 0.28075
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.503000020980835
Mean_Dice : 0.5619999766349792
Class_IoU : {0: 0.865, 1: 0.593, 2: 0.133, 3: 0.582, 4: 0.378, 5: 0.313, 6: 0.434, 7: 0.389, 8: 0.631, 9: 0.107, 10: 0.424, 11: 0.228, 12: 0.554, 13: 0.354, 14: 0.27, 15: 0.521, 16: 0.143, 17: 0.427, 18: 0.156, 19: 0.427, 20: 0.38}
Class_Dice : {0: 0.914, 1: 0.666, 2: 0.186, 3: 0.65, 4: 0.453, 5: 0.365, 6: 0.463, 7: 0.435, 8: 0.678, 9: 0.146, 10: 0.473, 11: 0.267, 12: 0.615, 13: 0.399, 14: 0.307, 15: 0.596, 16: 0.19, 17: 0.483, 18: 0.184, 19: 0.466, 20: 0.441}
## TESTING Restuls for Model: DeepLab + Loss: LovaszSoftmax + predict: T ##
test_loss : 0.28075
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.515999972820282
Mean_Dice : 0.5770000219345093
Class_IoU : {0: 0.865, 1: 0.615, 2: 0.152, 3: 0.595, 4: 0.379, 5: 0.327, 6: 0.478, 7: 0.448, 8: 0.648, 9: 0.111, 10: 0.456, 11: 0.235, 12: 0.594, 13: 0.382, 14: 0.318, 15: 0.536, 16: 0.145, 17: 0.462, 18: 0.166, 19: 0.49, 20: 0.406}
Class_Dice : {0: 0.914, 1: 0.69, 2: 0.212, 3: 0.666, 4: 0.455, 5: 0.382, 6: 0.51, 7: 0.501, 8: 0.696, 9: 0.151, 10: 0.508, 11: 0.275, 12: 0.66, 13: 0.431, 14: 0.362, 15: 0.613, 16: 0.193, 17: 0.522, 18: 0.197, 19: 0.535, 20: 0.471}
## TESTING Restuls for Model: DeepLab + Loss: LovaszSoftmax + predict: rankdice ##
test_loss : 0.28075
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.515999972820282
Mean_Dice : 0.578000009059906
Class_IoU : {0: 0.866, 1: 0.616, 2: 0.152, 3: 0.596, 4: 0.381, 5: 0.328, 6: 0.48, 7: 0.449, 8: 0.649, 9: 0.112, 10: 0.458, 11: 0.236, 12: 0.597, 13: 0.383, 14: 0.319, 15: 0.537, 16: 0.146, 17: 0.465, 18: 0.168, 19: 0.491, 20: 0.408}
Class_Dice : {0: 0.915, 1: 0.692, 2: 0.213, 3: 0.666, 4: 0.457, 5: 0.383, 6: 0.512, 7: 0.503, 8: 0.697, 9: 0.153, 10: 0.51, 11: 0.277, 12: 0.663, 13: 0.432, 14: 0.362, 15: 0.614, 16: 0.194, 17: 0.524, 18: 0.199, 19: 0.536, 20: 0.473}
## TESTING Restuls for Model: DeepLab + Loss: CE_DiceLoss + predict: max ##
test_loss : 0.35193
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5529999732971191
Mean_Dice : 0.6179999709129333
Class_IoU : {0: 0.866, 1: 0.502, 2: 0.252, 3: 0.619, 4: 0.393, 5: 0.326, 6: 0.528, 7: 0.45, 8: 0.66, 9: 0.124, 10: 0.442, 11: 0.259, 12: 0.534, 13: 0.454, 14: 0.508, 15: 0.543, 16: 0.187, 17: 0.463, 18: 0.213, 19: 0.511, 20: 0.411}
Class_Dice : {0: 0.916, 1: 0.557, 2: 0.349, 3: 0.687, 4: 0.463, 5: 0.379, 6: 0.563, 7: 0.505, 8: 0.714, 9: 0.172, 10: 0.485, 11: 0.303, 12: 0.592, 13: 0.512, 14: 0.577, 15: 0.622, 16: 0.243, 17: 0.52, 18: 0.25, 19: 0.555, 20: 0.469}
## TESTING Restuls for Model: DeepLab + Loss: CE_DiceLoss + predict: T ##
test_loss : 0.35193
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5680000185966492
Mean_Dice : 0.6340000033378601
Class_IoU : {0: 0.867, 1: 0.57, 2: 0.281, 3: 0.644, 4: 0.407, 5: 0.345, 6: 0.566, 7: 0.474, 8: 0.668, 9: 0.128, 10: 0.493, 11: 0.271, 12: 0.575, 13: 0.488, 14: 0.536, 15: 0.559, 16: 0.2, 17: 0.491, 18: 0.231, 19: 0.544, 20: 0.427}
Class_Dice : {0: 0.916, 1: 0.633, 2: 0.39, 3: 0.714, 4: 0.48, 5: 0.402, 6: 0.603, 7: 0.531, 8: 0.722, 9: 0.178, 10: 0.541, 11: 0.318, 12: 0.638, 13: 0.55, 14: 0.61, 15: 0.64, 16: 0.258, 17: 0.55, 18: 0.271, 19: 0.591, 20: 0.487}
## TESTING Restuls for Model: DeepLab + Loss: CE_DiceLoss + predict: rankdice ##
test_loss : 0.35193
Pixel_Accuracy : 0.9900000095367432
Mean_IoU : 0.5709999799728394
Mean_Dice : 0.6389999985694885
Class_IoU : {0: 0.841, 1: 0.571, 2: 0.292, 3: 0.644, 4: 0.411, 5: 0.353, 6: 0.572, 7: 0.484, 8: 0.674, 9: 0.148, 10: 0.484, 11: 0.281, 12: 0.58, 13: 0.491, 14: 0.54, 15: 0.561, 16: 0.212, 17: 0.5, 18: 0.24, 19: 0.547, 20: 0.433}
Class_Dice : {0: 0.89, 1: 0.635, 2: 0.404, 3: 0.715, 4: 0.484, 5: 0.41, 6: 0.61, 7: 0.543, 8: 0.729, 9: 0.205, 10: 0.533, 11: 0.33, 12: 0.643, 13: 0.554, 14: 0.614, 15: 0.643, 16: 0.277, 17: 0.562, 18: 0.282, 19: 0.595, 20: 0.494}
## TESTING Restuls for Model: DeepLab + Loss: BCEDiceLoss + predict: T ##
test_loss : 0.16872
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.125
Mean_Dice : 0.25099998712539673
Class_IoU : {0: 0.457, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.271, 7: 0.0, 8: 0.324, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.0, 14: 0.0, 15: 0.298, 16: 0.0, 17: 0.0, 18: 0.0, 19: 0.347, 20: 0.0}
Class_Dice : {0: 0.914, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.542, 7: 0.0, 8: 0.647, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.0, 14: 0.0, 15: 0.595, 16: 0.0, 17: 0.0, 18: 0.0, 19: 0.694, 20: 0.0}
## TESTING Restuls for Model: DeepLab + Loss: BCEDiceLoss + predict: rankdice ##
test_loss : 0.16872
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.12600000202655792
Mean_Dice : 0.25200000405311584
Class_IoU : {0: 0.456, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.275, 7: 0.0, 8: 0.325, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.0, 14: 0.0, 15: 0.298, 16: 0.0, 17: 0.0, 18: 0.0, 19: 0.35, 20: 0.0}
Class_Dice : {0: 0.912, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.549, 7: 0.0, 8: 0.651, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.0, 14: 0.0, 15: 0.596, 16: 0.0, 17: 0.0, 18: 0.0, 19: 0.7, 20: 0.0}
## TESTING Restuls for Model: DeepLab + Loss: BCEDiceLoss + predict: max ##
test_loss : 0.16872
Pixel_Accuracy : 0.9800000190734863
Mean_IoU : 0.08699999749660492
Mean_Dice : 0.17399999499320984
Class_IoU : {0: 0.45, 1: 0.001, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.03, 7: 0.01, 8: 0.066, 9: 0.0, 10: 0.0, 11: 0.001, 12: 0.024, 13: 0.001, 14: 0.0, 15: 0.119, 16: 0.003, 17: 0.0, 18: 0.005, 19: 0.04, 20: 0.0}
Class_Dice : {0: 0.899, 1: 0.002, 2: 0.0, 3: 0.0, 4: 0.001, 5: 0.0, 6: 0.059, 7: 0.021, 8: 0.132, 9: 0.0, 10: 0.0, 11: 0.003, 12: 0.048, 13: 0.003, 14: 0.0, 15: 0.238, 16: 0.006, 17: 0.0, 18: 0.01, 19: 0.079, 20: 0.001}
## TESTING Restuls for Model: UNet + Loss: CE_DiceLoss + predict: max ##
test_loss : 1.20597
Pixel_Accuracy : 0.9760000109672546
Mean_IoU : 0.06199999898672104
Mean_Dice : 0.07999999821186066
Class_IoU : {0: 0.752, 1: 0.069, 2: 0.0, 3: 0.002, 4: 0.018, 5: 0.001, 6: 0.053, 7: 0.047, 8: 0.096, 9: 0.002, 10: 0.026, 11: 0.005, 12: 0.054, 13: 0.033, 14: 0.037, 15: 0.159, 16: 0.004, 17: 0.05, 18: 0.006, 19: 0.058, 20: 0.022}
Class_Dice : {0: 0.839, 1: 0.084, 2: 0.0, 3: 0.003, 4: 0.026, 5: 0.002, 6: 0.07, 7: 0.06, 8: 0.123, 9: 0.003, 10: 0.039, 11: 0.008, 12: 0.078, 13: 0.047, 14: 0.049, 15: 0.201, 16: 0.007, 17: 0.066, 18: 0.01, 19: 0.078, 20: 0.032}
## TESTING Restuls for Model: UNet + Loss: CE_DiceLoss + predict: T ##
test_loss : 1.20597
Pixel_Accuracy : 0.9789999723434448
Mean_IoU : 0.09399999678134918
Mean_Dice : 0.12099999934434891
Class_IoU : {0: 0.752, 1: 0.101, 2: 0.0, 3: 0.001, 4: 0.029, 5: 0.0, 6: 0.068, 7: 0.063, 8: 0.099, 9: 0.0, 10: 0.017, 11: 0.003, 12: 0.03, 13: 0.028, 14: 0.05, 15: 0.179, 16: 0.003, 17: 0.065, 18: 0.003, 19: 0.076, 20: 0.024}
Class_Dice : {0: 0.838, 1: 0.122, 2: 0.0, 3: 0.001, 4: 0.041, 5: 0.0, 6: 0.092, 7: 0.082, 8: 0.133, 9: 0.0, 10: 0.027, 11: 0.006, 12: 0.05, 13: 0.044, 14: 0.072, 15: 0.226, 16: 0.006, 17: 0.092, 18: 0.005, 19: 0.106, 20: 0.037}
## TESTING Restuls for Model: UNet + Loss: CE_DiceLoss + predict: rankdice ##
test_loss : 1.20597
Pixel_Accuracy : 0.9779999852180481
Mean_IoU : 0.11100000143051147
Mean_Dice : 0.1420000046491623
Class_IoU : {0: 0.759, 1: 0.108, 2: 0.0, 3: 0.001, 4: 0.039, 5: 0.0, 6: 0.088, 7: 0.076, 8: 0.134, 9: 0.0, 10: 0.047, 11: 0.014, 12: 0.079, 13: 0.064, 14: 0.072, 15: 0.183, 16: 0.007, 17: 0.094, 18: 0.006, 19: 0.103, 20: 0.037}
Class_Dice : {0: 0.843, 1: 0.131, 2: 0.0, 3: 0.002, 4: 0.054, 5: 0.0, 6: 0.115, 7: 0.096, 8: 0.169, 9: 0.0, 10: 0.069, 11: 0.024, 12: 0.114, 13: 0.091, 14: 0.096, 15: 0.231, 16: 0.012, 17: 0.125, 18: 0.01, 19: 0.139, 20: 0.053}
## TESTING Restuls for Model: FCN8 + Loss: CrossEntropyLoss2d + predict: max ##
test_loss : 0.41173
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.3840000033378601
Mean_Dice : 0.453000009059906
Class_IoU : {0: 0.822, 1: 0.423, 2: 0.149, 3: 0.379, 4: 0.284, 5: 0.213, 6: 0.346, 7: 0.292, 8: 0.384, 9: 0.066, 10: 0.271, 11: 0.158, 12: 0.326, 13: 0.25, 14: 0.305, 15: 0.432, 16: 0.104, 17: 0.242, 18: 0.15, 19: 0.325, 20: 0.289}
Class_Dice : {0: 0.888, 1: 0.497, 2: 0.23, 3: 0.457, 4: 0.354, 5: 0.267, 6: 0.385, 7: 0.337, 8: 0.436, 9: 0.1, 10: 0.322, 11: 0.198, 12: 0.39, 13: 0.306, 14: 0.366, 15: 0.519, 16: 0.141, 17: 0.283, 18: 0.189, 19: 0.37, 20: 0.355}
## TESTING Restuls for Model: FCN8 + Loss: CrossEntropyLoss2d + predict: T ##
test_loss : 0.41172
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.4189999997615814
Mean_Dice : 0.4950000047683716
Class_IoU : {0: 0.822, 1: 0.496, 2: 0.133, 3: 0.435, 4: 0.334, 5: 0.23, 6: 0.406, 7: 0.337, 8: 0.422, 9: 0.068, 10: 0.333, 11: 0.176, 12: 0.391, 13: 0.305, 14: 0.377, 15: 0.462, 16: 0.108, 17: 0.31, 18: 0.181, 19: 0.364, 20: 0.32}
Class_Dice : {0: 0.888, 1: 0.583, 2: 0.21, 3: 0.525, 4: 0.417, 5: 0.285, 6: 0.452, 7: 0.388, 8: 0.479, 9: 0.102, 10: 0.395, 11: 0.221, 12: 0.469, 13: 0.373, 14: 0.454, 15: 0.554, 16: 0.147, 17: 0.363, 18: 0.228, 19: 0.415, 20: 0.392}
## TESTING Restuls for Model: FCN8 + Loss: CrossEntropyLoss2d + predict: rankdice ##
test_loss : 0.41173
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.4269999861717224
Mean_Dice : 0.5040000081062317
Class_IoU : {0: 0.824, 1: 0.495, 2: 0.165, 3: 0.439, 4: 0.336, 5: 0.228, 6: 0.413, 7: 0.353, 8: 0.427, 9: 0.087, 10: 0.345, 11: 0.185, 12: 0.402, 13: 0.311, 14: 0.384, 15: 0.466, 16: 0.123, 17: 0.313, 18: 0.199, 19: 0.369, 20: 0.331}
Class_Dice : {0: 0.889, 1: 0.583, 2: 0.253, 3: 0.53, 4: 0.419, 5: 0.286, 6: 0.458, 7: 0.404, 8: 0.483, 9: 0.129, 10: 0.409, 11: 0.231, 12: 0.482, 13: 0.38, 14: 0.461, 15: 0.559, 16: 0.165, 17: 0.366, 18: 0.25, 19: 0.42, 20: 0.404}
## TESTING Restuls for Model: FCN8 + Loss: FocalLoss + predict: max ##
test_loss : 0.20213
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.3930000066757202
Mean_Dice : 0.47200000286102295
Class_IoU : {0: 0.806, 1: 0.415, 2: 0.139, 3: 0.398, 4: 0.328, 5: 0.198, 6: 0.393, 7: 0.315, 8: 0.388, 9: 0.066, 10: 0.259, 11: 0.18, 12: 0.364, 13: 0.274, 14: 0.334, 15: 0.427, 16: 0.101, 17: 0.329, 18: 0.181, 19: 0.387, 20: 0.294}
Class_Dice : {0: 0.877, 1: 0.505, 2: 0.222, 3: 0.492, 4: 0.422, 5: 0.255, 6: 0.442, 7: 0.364, 8: 0.445, 9: 0.098, 10: 0.32, 11: 0.231, 12: 0.439, 13: 0.339, 14: 0.404, 15: 0.521, 16: 0.138, 17: 0.391, 18: 0.23, 19: 0.448, 20: 0.366}
## TESTING Restuls for Model: FCN8 + Loss: FocalLoss + predict: T ##
test_loss : 0.20213
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.4180000126361847
Mean_Dice : 0.5040000081062317
Class_IoU : {0: 0.799, 1: 0.486, 2: 0.11, 3: 0.457, 4: 0.357, 5: 0.204, 6: 0.452, 7: 0.342, 8: 0.443, 9: 0.05, 10: 0.333, 11: 0.187, 12: 0.42, 13: 0.307, 14: 0.433, 15: 0.457, 16: 0.095, 17: 0.396, 18: 0.172, 19: 0.433, 20: 0.306}
Class_Dice : {0: 0.872, 1: 0.593, 2: 0.181, 3: 0.565, 4: 0.46, 5: 0.262, 6: 0.512, 7: 0.393, 8: 0.509, 9: 0.074, 10: 0.42, 11: 0.241, 12: 0.512, 13: 0.385, 14: 0.527, 15: 0.556, 16: 0.132, 17: 0.479, 18: 0.222, 19: 0.504, 20: 0.383}
## TESTING Restuls for Model: FCN8 + Loss: FocalLoss + predict: rankdice ##
test_loss : 0.20213
Pixel_Accuracy : 0.9850000143051147
Mean_IoU : 0.42500001192092896
Mean_Dice : 0.5149999856948853
Class_IoU : {0: 0.81, 1: 0.454, 2: 0.136, 3: 0.453, 4: 0.344, 5: 0.213, 6: 0.468, 7: 0.363, 8: 0.449, 9: 0.069, 10: 0.364, 11: 0.236, 12: 0.448, 13: 0.324, 14: 0.446, 15: 0.452, 16: 0.12, 17: 0.412, 18: 0.217, 19: 0.447, 20: 0.298}
Class_Dice : {0: 0.88, 1: 0.57, 2: 0.219, 3: 0.568, 4: 0.451, 5: 0.274, 6: 0.529, 7: 0.415, 8: 0.517, 9: 0.103, 10: 0.454, 11: 0.296, 12: 0.541, 13: 0.405, 14: 0.544, 15: 0.555, 16: 0.16, 17: 0.495, 18: 0.272, 19: 0.521, 20: 0.379}
## TESTING Restuls for Model: FCN8 + Loss: LovaszSoftmax + predict: max ##
test_loss : 0.39404
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.32199999690055847
Mean_Dice : 0.37299999594688416
Class_IoU : {0: 0.826, 1: 0.376, 2: 0.067, 3: 0.373, 4: 0.22, 5: 0.148, 6: 0.255, 7: 0.256, 8: 0.37, 9: 0.053, 10: 0.257, 11: 0.107, 12: 0.324, 13: 0.222, 14: 0.209, 15: 0.416, 16: 0.082, 17: 0.249, 18: 0.106, 19: 0.279, 20: 0.215}
Class_Dice : {0: 0.889, 1: 0.426, 2: 0.099, 3: 0.439, 4: 0.269, 5: 0.185, 6: 0.281, 7: 0.3, 8: 0.417, 9: 0.079, 10: 0.303, 11: 0.132, 12: 0.375, 13: 0.267, 14: 0.246, 15: 0.495, 16: 0.11, 17: 0.288, 18: 0.13, 19: 0.317, 20: 0.258}
## TESTING Restuls for Model: FCN8 + Loss: LovaszSoftmax + predict: T ##
test_loss : 0.39404
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.34299999475479126
Mean_Dice : 0.39800000190734863
Class_IoU : {0: 0.825, 1: 0.408, 2: 0.081, 3: 0.399, 4: 0.242, 5: 0.168, 6: 0.27, 7: 0.282, 8: 0.378, 9: 0.058, 10: 0.28, 11: 0.116, 12: 0.347, 13: 0.242, 14: 0.249, 15: 0.426, 16: 0.094, 17: 0.269, 18: 0.12, 19: 0.291, 20: 0.223}
Class_Dice : {0: 0.889, 1: 0.462, 2: 0.12, 3: 0.469, 4: 0.295, 5: 0.209, 6: 0.297, 7: 0.33, 8: 0.426, 9: 0.086, 10: 0.33, 11: 0.144, 12: 0.402, 13: 0.29, 14: 0.293, 15: 0.507, 16: 0.127, 17: 0.311, 18: 0.146, 19: 0.331, 20: 0.267}
## TESTING Restuls for Model: FCN8 + Loss: LovaszSoftmax + predict: rankdice ##
test_loss : 0.39404
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.3440000116825104
Mean_Dice : 0.4000000059604645
Class_IoU : {0: 0.826, 1: 0.408, 2: 0.083, 3: 0.403, 4: 0.243, 5: 0.17, 6: 0.271, 7: 0.283, 8: 0.38, 9: 0.062, 10: 0.282, 11: 0.118, 12: 0.349, 13: 0.242, 14: 0.249, 15: 0.427, 16: 0.096, 17: 0.273, 18: 0.12, 19: 0.292, 20: 0.225}
Class_Dice : {0: 0.889, 1: 0.463, 2: 0.122, 3: 0.474, 4: 0.296, 5: 0.211, 6: 0.299, 7: 0.332, 8: 0.428, 9: 0.092, 10: 0.332, 11: 0.145, 12: 0.403, 13: 0.291, 14: 0.294, 15: 0.508, 16: 0.129, 17: 0.315, 18: 0.147, 19: 0.332, 20: 0.27}
## TESTING Restuls for Model: FCN8 + Loss: BCEWithLogitsLoss2d + predict: T ##
test_loss : 0.03748
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.23100000619888306
Mean_Dice : 0.4620000123977661
Class_IoU : {0: 0.437, 1: 0.261, 2: 0.05, 3: 0.233, 4: 0.185, 5: 0.119, 6: 0.206, 7: 0.19, 8: 0.223, 9: 0.045, 10: 0.178, 11: 0.115, 12: 0.213, 13: 0.168, 14: 0.235, 15: 0.276, 16: 0.063, 17: 0.188, 18: 0.111, 19: 0.215, 20: 0.178}
Class_Dice : {0: 0.874, 1: 0.522, 2: 0.101, 3: 0.465, 4: 0.371, 5: 0.238, 6: 0.413, 7: 0.381, 8: 0.445, 9: 0.089, 10: 0.357, 11: 0.229, 12: 0.426, 13: 0.335, 14: 0.469, 15: 0.552, 16: 0.127, 17: 0.376, 18: 0.222, 19: 0.43, 20: 0.356}
## TESTING Restuls for Model: FCN8 + Loss: BCEWithLogitsLoss2d + predict: max ##
test_loss : 0.03748
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.22100000083446503
Mean_Dice : 0.44200000166893005
Class_IoU : {0: 0.441, 1: 0.233, 2: 0.097, 3: 0.236, 4: 0.172, 5: 0.119, 6: 0.183, 7: 0.178, 8: 0.207, 9: 0.046, 10: 0.153, 11: 0.104, 12: 0.201, 13: 0.142, 14: 0.18, 15: 0.256, 16: 0.062, 17: 0.166, 18: 0.094, 19: 0.183, 20: 0.151}
Class_Dice : {0: 0.881, 1: 0.465, 2: 0.195, 3: 0.472, 4: 0.345, 5: 0.238, 6: 0.366, 7: 0.356, 8: 0.414, 9: 0.092, 10: 0.306, 11: 0.208, 12: 0.403, 13: 0.284, 14: 0.361, 15: 0.511, 16: 0.124, 17: 0.331, 18: 0.188, 19: 0.367, 20: 0.302}
## TESTING Restuls for Model: FCN8 + Loss: BCEWithLogitsLoss2d + predict: rankdice ##
test_loss : 0.03748
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.23800000548362732
Mean_Dice : 0.47699999809265137
Class_IoU : {0: 0.44, 1: 0.265, 2: 0.072, 3: 0.241, 4: 0.192, 5: 0.123, 6: 0.21, 7: 0.199, 8: 0.228, 9: 0.06, 10: 0.185, 11: 0.125, 12: 0.222, 13: 0.175, 14: 0.242, 15: 0.28, 16: 0.074, 17: 0.197, 18: 0.123, 19: 0.22, 20: 0.187}
Class_Dice : {0: 0.88, 1: 0.529, 2: 0.145, 3: 0.483, 4: 0.383, 5: 0.247, 6: 0.421, 7: 0.398, 8: 0.456, 9: 0.121, 10: 0.371, 11: 0.251, 12: 0.444, 13: 0.349, 14: 0.485, 15: 0.561, 16: 0.149, 17: 0.393, 18: 0.247, 19: 0.441, 20: 0.373}
## TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: max ##
test_loss : 0.23093
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5490000247955322
Mean_Dice : 0.6159999966621399
Class_IoU : {0: 0.871, 1: 0.619, 2: 0.253, 3: 0.653, 4: 0.359, 5: 0.306, 6: 0.581, 7: 0.463, 8: 0.677, 9: 0.117, 10: 0.532, 11: 0.23, 12: 0.501, 13: 0.495, 14: 0.612, 15: 0.539, 16: 0.211, 17: 0.496, 18: 0.217, 19: 0.56, 20: 0.406}
Class_Dice : {0: 0.919, 1: 0.695, 2: 0.357, 3: 0.732, 4: 0.429, 5: 0.361, 6: 0.618, 7: 0.515, 8: 0.731, 9: 0.161, 10: 0.59, 11: 0.273, 12: 0.555, 13: 0.564, 14: 0.696, 15: 0.616, 16: 0.278, 17: 0.558, 18: 0.251, 19: 0.613, 20: 0.466}
## TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: T ##
test_loss : 0.23093
Pixel_Accuracy : 0.9919999837875366
Mean_IoU : 0.5600000023841858
Mean_Dice : 0.6290000081062317
Class_IoU : {0: 0.871, 1: 0.642, 2: 0.274, 3: 0.675, 4: 0.371, 5: 0.327, 6: 0.603, 7: 0.476, 8: 0.685, 9: 0.125, 10: 0.558, 11: 0.24, 12: 0.543, 13: 0.516, 14: 0.662, 15: 0.566, 16: 0.214, 17: 0.52, 18: 0.242, 19: 0.618, 20: 0.405}
Class_Dice : {0: 0.919, 1: 0.721, 2: 0.385, 3: 0.757, 4: 0.442, 5: 0.384, 6: 0.641, 7: 0.529, 8: 0.74, 9: 0.173, 10: 0.617, 11: 0.285, 12: 0.601, 13: 0.588, 14: 0.752, 15: 0.647, 16: 0.281, 17: 0.586, 18: 0.281, 19: 0.676, 20: 0.465}
## TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: rankdice ##
test_loss : 0.23093
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5630000233650208
Mean_Dice : 0.6330000162124634
Class_IoU : {0: 0.872, 1: 0.643, 2: 0.272, 3: 0.677, 4: 0.371, 5: 0.335, 6: 0.606, 7: 0.481, 8: 0.689, 9: 0.135, 10: 0.563, 11: 0.247, 12: 0.544, 13: 0.523, 14: 0.667, 15: 0.568, 16: 0.226, 17: 0.525, 18: 0.248, 19: 0.622, 20: 0.41}
Class_Dice : {0: 0.919, 1: 0.722, 2: 0.384, 3: 0.759, 4: 0.443, 5: 0.393, 6: 0.645, 7: 0.536, 8: 0.744, 9: 0.186, 10: 0.624, 11: 0.294, 12: 0.602, 13: 0.595, 14: 0.758, 15: 0.65, 16: 0.295, 17: 0.592, 18: 0.286, 19: 0.68, 20: 0.471}
## TESTING Restuls for Model: PSPNet + Loss: CrossEntropyLoss2d + predict: max ##
test_loss : 0.23951
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5590000152587891
Mean_Dice : 0.6320000290870667
Class_IoU : {0: 0.861, 1: 0.598, 2: 0.259, 3: 0.669, 4: 0.384, 5: 0.351, 6: 0.627, 7: 0.448, 8: 0.667, 9: 0.124, 10: 0.534, 11: 0.273, 12: 0.529, 13: 0.535, 14: 0.553, 15: 0.551, 16: 0.201, 17: 0.458, 18: 0.246, 19: 0.656, 20: 0.405}
Class_Dice : {0: 0.913, 1: 0.672, 2: 0.37, 3: 0.746, 4: 0.46, 5: 0.424, 6: 0.669, 7: 0.504, 8: 0.724, 9: 0.169, 10: 0.595, 11: 0.325, 12: 0.591, 13: 0.619, 14: 0.635, 15: 0.641, 16: 0.267, 17: 0.52, 18: 0.294, 19: 0.718, 20: 0.474}
## TESTING Restuls for Model: PSPNet + Loss: CrossEntropyLoss2d + predict: T ##
test_loss : 0.23951
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5709999799728394
Mean_Dice : 0.6460000276565552
Class_IoU : {0: 0.862, 1: 0.634, 2: 0.259, 3: 0.682, 4: 0.427, 5: 0.358, 6: 0.683, 7: 0.475, 8: 0.692, 9: 0.128, 10: 0.586, 11: 0.275, 12: 0.579, 13: 0.553, 14: 0.606, 15: 0.577, 16: 0.202, 17: 0.474, 18: 0.25, 19: 0.71, 20: 0.414}
Class_Dice : {0: 0.914, 1: 0.712, 2: 0.371, 3: 0.76, 4: 0.511, 5: 0.428, 6: 0.728, 7: 0.535, 8: 0.75, 9: 0.175, 10: 0.653, 11: 0.329, 12: 0.646, 13: 0.639, 14: 0.697, 15: 0.67, 16: 0.269, 17: 0.539, 18: 0.298, 19: 0.777, 20: 0.484}
## TESTING Restuls for Model: PSPNet + Loss: CrossEntropyLoss2d + predict: rankdice ##
test_loss : 0.2395
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.578000009059906
Mean_Dice : 0.6539999842643738
Class_IoU : {0: 0.863, 1: 0.634, 2: 0.273, 3: 0.687, 4: 0.429, 5: 0.368, 6: 0.696, 7: 0.49, 8: 0.697, 9: 0.144, 10: 0.599, 11: 0.292, 12: 0.587, 13: 0.566, 14: 0.626, 15: 0.581, 16: 0.22, 17: 0.479, 18: 0.269, 19: 0.711, 20: 0.42}
Class_Dice : {0: 0.914, 1: 0.713, 2: 0.387, 3: 0.766, 4: 0.513, 5: 0.442, 6: 0.741, 7: 0.55, 8: 0.755, 9: 0.196, 10: 0.665, 11: 0.345, 12: 0.655, 13: 0.653, 14: 0.716, 15: 0.674, 16: 0.291, 17: 0.543, 18: 0.32, 19: 0.779, 20: 0.492}
## TESTING Restuls for Model: PSPNet + Loss: FocalLoss + predict: max ##
test_loss : 0.10367
Pixel_Accuracy : 0.9900000095367432
Mean_IoU : 0.5609999895095825
Mean_Dice : 0.6389999985694885
Class_IoU : {0: 0.844, 1: 0.597, 2: 0.167, 3: 0.651, 4: 0.491, 5: 0.297, 6: 0.625, 7: 0.449, 8: 0.649, 9: 0.138, 10: 0.486, 11: 0.275, 12: 0.566, 13: 0.523, 14: 0.533, 15: 0.552, 16: 0.183, 17: 0.511, 18: 0.254, 19: 0.664, 20: 0.421}
Class_Dice : {0: 0.903, 1: 0.685, 2: 0.26, 3: 0.743, 4: 0.592, 5: 0.358, 6: 0.669, 7: 0.505, 8: 0.711, 9: 0.194, 10: 0.542, 11: 0.336, 12: 0.633, 13: 0.605, 14: 0.617, 15: 0.647, 16: 0.239, 17: 0.578, 18: 0.309, 19: 0.729, 20: 0.511}
## TESTING Restuls for Model: PSPNet + Loss: FocalLoss + predict: T ##
test_loss : 0.10367
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5609999895095825
Mean_Dice : 0.6399999856948853
Class_IoU : {0: 0.848, 1: 0.592, 2: 0.124, 3: 0.652, 4: 0.491, 5: 0.3, 6: 0.675, 7: 0.456, 8: 0.679, 9: 0.116, 10: 0.537, 11: 0.274, 12: 0.634, 13: 0.583, 14: 0.567, 15: 0.554, 16: 0.173, 17: 0.554, 18: 0.244, 19: 0.689, 20: 0.416}
Class_Dice : {0: 0.905, 1: 0.684, 2: 0.196, 3: 0.743, 4: 0.595, 5: 0.362, 6: 0.723, 7: 0.511, 8: 0.741, 9: 0.167, 10: 0.601, 11: 0.336, 12: 0.71, 13: 0.673, 14: 0.655, 15: 0.65, 16: 0.224, 17: 0.628, 18: 0.298, 19: 0.758, 20: 0.507}
## TESTING Restuls for Model: PSPNet + Loss: FocalLoss + predict: rankdice ##
test_loss : 0.10367
Pixel_Accuracy : 0.9900000095367432
Mean_IoU : 0.5849999785423279
Mean_Dice : 0.6660000085830688
Class_IoU : {0: 0.844, 1: 0.641, 2: 0.206, 3: 0.664, 4: 0.515, 5: 0.314, 6: 0.688, 7: 0.479, 8: 0.687, 9: 0.161, 10: 0.56, 11: 0.324, 12: 0.649, 13: 0.601, 14: 0.579, 15: 0.576, 16: 0.207, 17: 0.586, 18: 0.29, 19: 0.704, 20: 0.454}
Class_Dice : {0: 0.902, 1: 0.727, 2: 0.305, 3: 0.758, 4: 0.619, 5: 0.376, 6: 0.735, 7: 0.536, 8: 0.754, 9: 0.222, 10: 0.623, 11: 0.386, 12: 0.725, 13: 0.695, 14: 0.67, 15: 0.672, 16: 0.269, 17: 0.66, 18: 0.346, 19: 0.773, 20: 0.541}
## TESTING Restuls for Model: PSPNet + Loss: LovaszSoftmax + predict: max ##
test_loss : 0.29028
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5410000085830688
Mean_Dice : 0.6079999804496765
Class_IoU : {0: 0.859, 1: 0.566, 2: 0.211, 3: 0.59, 4: 0.46, 5: 0.252, 6: 0.5, 7: 0.47, 8: 0.533, 9: 0.143, 10: 0.565, 11: 0.242, 12: 0.524, 13: 0.416, 14: 0.512, 15: 0.552, 16: 0.176, 17: 0.526, 18: 0.21, 19: 0.581, 20: 0.382}
Class_Dice : {0: 0.911, 1: 0.633, 2: 0.294, 3: 0.657, 4: 0.545, 5: 0.301, 6: 0.537, 7: 0.534, 8: 0.576, 9: 0.193, 10: 0.63, 11: 0.29, 12: 0.58, 13: 0.471, 14: 0.587, 15: 0.636, 16: 0.229, 17: 0.589, 18: 0.248, 19: 0.636, 20: 0.438}
## TESTING Restuls for Model: PSPNet + Loss: LovaszSoftmax + predict: T ##
test_loss : 0.29028
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5519999861717224
Mean_Dice : 0.6200000047683716
Class_IoU : {0: 0.859, 1: 0.585, 2: 0.219, 3: 0.613, 4: 0.464, 5: 0.257, 6: 0.541, 7: 0.485, 8: 0.567, 9: 0.15, 10: 0.575, 11: 0.252, 12: 0.551, 13: 0.47, 14: 0.525, 15: 0.562, 16: 0.182, 17: 0.57, 18: 0.218, 19: 0.629, 20: 0.408}
Class_Dice : {0: 0.911, 1: 0.655, 2: 0.305, 3: 0.683, 4: 0.549, 5: 0.306, 6: 0.582, 7: 0.551, 8: 0.612, 9: 0.202, 10: 0.641, 11: 0.303, 12: 0.609, 13: 0.533, 14: 0.602, 15: 0.648, 16: 0.237, 17: 0.637, 18: 0.258, 19: 0.689, 20: 0.467}
## TESTING Restuls for Model: PSPNet + Loss: LovaszSoftmax + predict: rankdice ##
test_loss : 0.29028
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.5540000200271606
Mean_Dice : 0.621999979019165
Class_IoU : {0: 0.859, 1: 0.588, 2: 0.22, 3: 0.615, 4: 0.468, 5: 0.259, 6: 0.543, 7: 0.489, 8: 0.568, 9: 0.153, 10: 0.585, 11: 0.257, 12: 0.553, 13: 0.472, 14: 0.529, 15: 0.563, 16: 0.184, 17: 0.573, 18: 0.224, 19: 0.634, 20: 0.409}
Class_Dice : {0: 0.911, 1: 0.657, 2: 0.306, 3: 0.684, 4: 0.555, 5: 0.309, 6: 0.584, 7: 0.555, 8: 0.613, 9: 0.207, 10: 0.652, 11: 0.308, 12: 0.611, 13: 0.535, 14: 0.605, 15: 0.649, 16: 0.24, 17: 0.641, 18: 0.265, 19: 0.694, 20: 0.469}
## TESTING Restuls for Model: PSPNet + Loss: DiceLoss + predict: max ##
test_loss : 0.11046
Pixel_Accuracy : 0.9900000095367432
Mean_IoU : 0.5320000052452087
Mean_Dice : 0.5879999995231628
Class_IoU : {0: 0.874, 1: 0.678, 2: 0.0, 3: 0.675, 4: 0.435, 5: 0.0, 6: 0.661, 7: 0.427, 8: 0.692, 9: 0.091, 10: 0.564, 11: 0.0, 12: 0.597, 13: 0.571, 14: 0.486, 15: 0.531, 16: 0.127, 17: 0.494, 18: 0.0, 19: 0.609, 20: 0.0}
Class_Dice : {0: 0.922, 1: 0.753, 2: 0.0, 3: 0.742, 4: 0.511, 5: 0.0, 6: 0.699, 7: 0.473, 8: 0.738, 9: 0.128, 10: 0.621, 11: 0.0, 12: 0.654, 13: 0.644, 14: 0.555, 15: 0.607, 16: 0.162, 17: 0.548, 18: 0.0, 19: 0.663, 20: 0.0}
## TESTING Restuls for Model: PSPNet + Loss: DiceLoss + predict: T ##
test_loss : 0.11046
Pixel_Accuracy : 0.9900000095367432
Mean_IoU : 0.5400000214576721
Mean_Dice : 0.5960000157356262
Class_IoU : {0: 0.874, 1: 0.684, 2: 0.0, 3: 0.695, 4: 0.474, 5: 0.0, 6: 0.716, 7: 0.465, 8: 0.714, 9: 0.096, 10: 0.589, 11: 0.0, 12: 0.638, 13: 0.597, 14: 0.52, 15: 0.543, 16: 0.138, 17: 0.541, 18: 0.0, 19: 0.667, 20: 0.0}
Class_Dice : {0: 0.922, 1: 0.76, 2: 0.0, 3: 0.766, 4: 0.556, 5: 0.0, 6: 0.759, 7: 0.515, 8: 0.762, 9: 0.136, 10: 0.648, 11: 0.0, 12: 0.699, 13: 0.673, 14: 0.593, 15: 0.62, 16: 0.176, 17: 0.601, 18: 0.0, 19: 0.727, 20: 0.0}
## TESTING Restuls for Model: PSPNet + Loss: DiceLoss + predict: rankdice ##
test_loss : 0.11046
Pixel_Accuracy : 0.9900000095367432
Mean_IoU : 0.5429999828338623
Mean_Dice : 0.6000000238418579
Class_IoU : {0: 0.875, 1: 0.686, 2: 0.0, 3: 0.701, 4: 0.478, 5: 0.0, 6: 0.719, 7: 0.473, 8: 0.717, 9: 0.101, 10: 0.602, 11: 0.0, 12: 0.643, 13: 0.606, 14: 0.523, 15: 0.544, 16: 0.144, 17: 0.552, 18: 0.0, 19: 0.669, 20: 0.0}
Class_Dice : {0: 0.923, 1: 0.761, 2: 0.0, 3: 0.77, 4: 0.561, 5: 0.0, 6: 0.761, 7: 0.524, 8: 0.765, 9: 0.142, 10: 0.661, 11: 0.0, 12: 0.703, 13: 0.682, 14: 0.597, 15: 0.621, 16: 0.184, 17: 0.611, 18: 0.0, 19: 0.728, 20: 0.0}
## TESTING Restuls for Model: PSPNet + Loss: BCEWithLogitsLoss2d + predict: T ##
test_loss : 0.02315
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.32100000977516174
Mean_Dice : 0.6420000195503235
Class_IoU : {0: 0.455, 1: 0.365, 2: 0.073, 3: 0.371, 4: 0.278, 5: 0.196, 6: 0.374, 7: 0.244, 8: 0.366, 9: 0.051, 10: 0.324, 11: 0.159, 12: 0.359, 13: 0.335, 14: 0.335, 15: 0.337, 16: 0.128, 17: 0.311, 18: 0.168, 19: 0.401, 20: 0.268}
Class_Dice : {0: 0.909, 1: 0.729, 2: 0.146, 3: 0.742, 4: 0.555, 5: 0.391, 6: 0.748, 7: 0.489, 8: 0.731, 9: 0.102, 10: 0.649, 11: 0.317, 12: 0.717, 13: 0.67, 14: 0.669, 15: 0.674, 16: 0.255, 17: 0.621, 18: 0.337, 19: 0.803, 20: 0.537}
## TESTING Restuls for Model: PSPNet + Loss: BCEWithLogitsLoss2d + predict: max ##
test_loss : 0.02315
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.32600000500679016
Mean_Dice : 0.6520000100135803
Class_IoU : {0: 0.457, 1: 0.347, 2: 0.181, 3: 0.367, 4: 0.285, 5: 0.195, 6: 0.328, 7: 0.244, 8: 0.375, 9: 0.083, 10: 0.316, 11: 0.16, 12: 0.331, 13: 0.328, 14: 0.292, 15: 0.326, 16: 0.139, 17: 0.276, 18: 0.163, 19: 0.376, 20: 0.245}
Class_Dice : {0: 0.914, 1: 0.694, 2: 0.362, 3: 0.733, 4: 0.571, 5: 0.391, 6: 0.657, 7: 0.488, 8: 0.75, 9: 0.165, 10: 0.631, 11: 0.319, 12: 0.663, 13: 0.655, 14: 0.583, 15: 0.651, 16: 0.278, 17: 0.552, 18: 0.327, 19: 0.752, 20: 0.49}
## TESTING Restuls for Model: PSPNet + Loss: BCEWithLogitsLoss2d + predict: rankdice ##
test_loss : 0.02315
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.33500000834465027
Mean_Dice : 0.6710000038146973
Class_IoU : {0: 0.457, 1: 0.376, 2: 0.115, 3: 0.381, 4: 0.305, 5: 0.206, 6: 0.379, 7: 0.259, 8: 0.376, 9: 0.073, 10: 0.342, 11: 0.176, 12: 0.372, 13: 0.354, 14: 0.351, 15: 0.348, 16: 0.143, 17: 0.334, 18: 0.194, 19: 0.411, 20: 0.282}
Class_Dice : {0: 0.914, 1: 0.753, 2: 0.231, 3: 0.763, 4: 0.61, 5: 0.411, 6: 0.759, 7: 0.517, 8: 0.751, 9: 0.145, 10: 0.684, 11: 0.353, 12: 0.744, 13: 0.709, 14: 0.701, 15: 0.697, 16: 0.286, 17: 0.669, 18: 0.389, 19: 0.821, 20: 0.564}
## TESTING Restuls for Model: PSPNet + Loss: BCEDiceLoss + predict: T ##
test_loss : 0.10597
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.3160000145435333
Mean_Dice : 0.6330000162124634
Class_IoU : {0: 0.456, 1: 0.365, 2: 0.0, 3: 0.383, 4: 0.277, 5: 0.197, 6: 0.387, 7: 0.253, 8: 0.357, 9: 0.0, 10: 0.313, 11: 0.139, 12: 0.346, 13: 0.329, 14: 0.331, 15: 0.32, 16: 0.129, 17: 0.31, 18: 0.162, 19: 0.387, 20: 0.261}
Class_Dice : {0: 0.913, 1: 0.731, 2: 0.0, 3: 0.765, 4: 0.554, 5: 0.394, 6: 0.774, 7: 0.506, 8: 0.713, 9: 0.0, 10: 0.626, 11: 0.277, 12: 0.693, 13: 0.659, 14: 0.661, 15: 0.64, 16: 0.258, 17: 0.62, 18: 0.324, 19: 0.773, 20: 0.523}
## TESTING Restuls for Model: PSPNet + Loss: BCEDiceLoss + predict: max ##
test_loss : 0.10597
Pixel_Accuracy : 0.9900000095367432
Mean_IoU : 0.27000001072883606
Mean_Dice : 0.5400000214576721
Class_IoU : {0: 0.456, 1: 0.228, 2: 0.0, 3: 0.313, 4: 0.134, 5: 0.135, 6: 0.181, 7: 0.136, 8: 0.302, 9: 0.0, 10: 0.179, 11: 0.092, 12: 0.211, 13: 0.147, 14: 0.14, 15: 0.266, 16: 0.07, 17: 0.165, 18: 0.08, 19: 0.168, 20: 0.122}
Class_Dice : {0: 0.913, 1: 0.456, 2: 0.0, 3: 0.626, 4: 0.268, 5: 0.271, 6: 0.363, 7: 0.272, 8: 0.605, 9: 0.0, 10: 0.357, 11: 0.183, 12: 0.422, 13: 0.294, 14: 0.279, 15: 0.533, 16: 0.14, 17: 0.33, 18: 0.16, 19: 0.336, 20: 0.243}
## TESTING Restuls for Model: PSPNet + Loss: BCEDiceLoss + predict: rankdice ##
test_loss : 0.10597
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.32199999690055847
Mean_Dice : 0.6430000066757202
Class_IoU : {0: 0.448, 1: 0.368, 2: 0.0, 3: 0.388, 4: 0.289, 5: 0.206, 6: 0.393, 7: 0.265, 8: 0.36, 9: 0.0, 10: 0.325, 11: 0.153, 12: 0.354, 13: 0.337, 14: 0.335, 15: 0.322, 16: 0.138, 17: 0.312, 18: 0.176, 19: 0.39, 20: 0.266}
Class_Dice : {0: 0.896, 1: 0.736, 2: 0.0, 3: 0.776, 4: 0.579, 5: 0.413, 6: 0.785, 7: 0.53, 8: 0.721, 9: 0.0, 10: 0.65, 11: 0.306, 12: 0.708, 13: 0.675, 14: 0.671, 15: 0.644, 16: 0.277, 17: 0.625, 18: 0.351, 19: 0.781, 20: 0.533}
TESTING Results for CityScapes dataset ## TESTING Restuls for Model: DeepLab + Loss: FocalLoss + predict: T ##
test_loss : 0.07274
Pixel_Accuracy : 0.9890000224113464
Mean_IoU : 0.4659999907016754
Mean_Dice : 0.5410000085830688
Class_IoU : {0: 0.779, 1: 0.444, 2: 0.75, 3: 0.116, 4: 0.099, 5: 0.24, 6: 0.198, 7: 0.287, 8: 0.837, 9: 0.181, 10: 0.762, 11: 0.281, 12: 0.181, 13: 0.763, 14: 0.218, 15: 0.387, 16: 0.229, 17: 0.121, 18: 0.291}
Class_Dice : {0: 0.861, 1: 0.544, 2: 0.838, 3: 0.147, 4: 0.135, 5: 0.354, 6: 0.267, 7: 0.379, 8: 0.901, 9: 0.231, 10: 0.823, 11: 0.355, 12: 0.242, 13: 0.835, 14: 0.254, 15: 0.437, 16: 0.268, 17: 0.158, 18: 0.377}
## TESTING Restuls for Model: DeepLab + Loss: FocalLoss + predict: max ##
test_loss : 0.07274
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.4560000002384186
Mean_Dice : 0.5329999923706055
Class_IoU : {0: 0.769, 1: 0.475, 2: 0.742, 3: 0.102, 4: 0.086, 5: 0.275, 6: 0.196, 7: 0.329, 8: 0.832, 9: 0.207, 10: 0.758, 11: 0.331, 12: 0.217, 13: 0.739, 14: 0.204, 15: 0.308, 16: 0.124, 17: 0.124, 18: 0.273}
Class_Dice : {0: 0.855, 1: 0.578, 2: 0.831, 3: 0.132, 4: 0.118, 5: 0.404, 6: 0.266, 7: 0.427, 8: 0.898, 9: 0.265, 10: 0.82, 11: 0.42, 12: 0.286, 13: 0.817, 14: 0.237, 15: 0.348, 16: 0.153, 17: 0.162, 18: 0.358}
## TESTING Restuls for Model: DeepLab + Loss: FocalLoss + predict: rankdice ## truncate mean
test_loss : 0.07274
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.4869999885559082
Mean_Dice : 0.5649999976158142
Class_IoU : {0: 0.777, 1: 0.495, 2: 0.754, 3: 0.135, 4: 0.115, 5: 0.283, 6: 0.223, 7: 0.333, 8: 0.832, 9: 0.213, 10: 0.772, 11: 0.323, 12: 0.219, 13: 0.759, 14: 0.248, 15: 0.415, 16: 0.23, 17: 0.147, 18: 0.301}
Class_Dice : {0: 0.86, 1: 0.595, 2: 0.843, 3: 0.17, 4: 0.156, 5: 0.415, 6: 0.302, 7: 0.43, 8: 0.899, 9: 0.266, 10: 0.834, 11: 0.404, 12: 0.283, 13: 0.836, 14: 0.285, 15: 0.464, 16: 0.272, 17: 0.19, 18: 0.39}
## TESTING Restuls for Model: DeepLab + Loss: FocalLoss + predict: rankdice ##
test_loss : 0.07274
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.4729999899864197
Mean_Dice : 0.5519999861717224
Class_IoU : {0: 0.777, 1: 0.48, 2: 0.745, 3: 0.133, 4: 0.11, 5: 0.195, 6: 0.184, 7: 0.321, 8: 0.828, 9: 0.219, 10: 0.768, 11: 0.331, 12: 0.222, 13: 0.744, 14: 0.257, 15: 0.422, 16: 0.224, 17: 0.162, 18: 0.282}
Class_Dice : {0: 0.86, 1: 0.582, 2: 0.836, 3: 0.17, 4: 0.152, 5: 0.306, 6: 0.263, 7: 0.425, 8: 0.896, 9: 0.275, 10: 0.832, 11: 0.416, 12: 0.292, 13: 0.823, 14: 0.294, 15: 0.471, 16: 0.267, 17: 0.203, 18: 0.371}
## TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: max ##
test_loss : 0.16293
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.4659999907016754
Mean_Dice : 0.5419999957084656
Class_IoU : {0: 0.766, 1: 0.494, 2: 0.748, 3: 0.123, 4: 0.103, 5: 0.303, 6: 0.206, 7: 0.354, 8: 0.842, 9: 0.217, 10: 0.77, 11: 0.337, 12: 0.222, 13: 0.724, 14: 0.195, 15: 0.252, 16: 0.124, 17: 0.131, 18: 0.272}
Class_Dice : {0: 0.853, 1: 0.592, 2: 0.835, 3: 0.156, 4: 0.137, 5: 0.437, 6: 0.277, 7: 0.453, 8: 0.905, 9: 0.276, 10: 0.831, 11: 0.425, 12: 0.29, 13: 0.801, 14: 0.229, 15: 0.286, 16: 0.143, 17: 0.165, 18: 0.354}
## TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: T ##
test_loss : 0.16292
Pixel_Accuracy : 0.9890000224113464
Mean_IoU : 0.48399999737739563
Mean_Dice : 0.5600000023841858
Class_IoU : {0: 0.769, 1: 0.484, 2: 0.761, 3: 0.127, 4: 0.113, 5: 0.3, 6: 0.223, 7: 0.346, 8: 0.844, 9: 0.21, 10: 0.775, 11: 0.335, 12: 0.223, 13: 0.747, 14: 0.222, 15: 0.342, 16: 0.204, 17: 0.135, 18: 0.294}
Class_Dice : {0: 0.855, 1: 0.58, 2: 0.846, 3: 0.159, 4: 0.149, 5: 0.43, 6: 0.296, 7: 0.443, 8: 0.906, 9: 0.265, 10: 0.835, 11: 0.419, 12: 0.289, 13: 0.819, 14: 0.257, 15: 0.386, 16: 0.23, 17: 0.172, 18: 0.376}
## TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: rankdice ##
test_loss : 0.16292
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.49799999594688416
Mean_Dice : 0.578000009059906
Class_IoU : {0: 0.767, 1: 0.518, 2: 0.764, 3: 0.157, 4: 0.135, 5: 0.317, 6: 0.232, 7: 0.375, 8: 0.847, 9: 0.242, 10: 0.785, 11: 0.36, 12: 0.252, 13: 0.741, 14: 0.243, 15: 0.37, 16: 0.213, 17: 0.155, 18: 0.294}
Class_Dice : {0: 0.853, 1: 0.616, 2: 0.849, 3: 0.197, 4: 0.178, 5: 0.458, 6: 0.312, 7: 0.478, 8: 0.908, 9: 0.304, 10: 0.847, 11: 0.451, 12: 0.325, 13: 0.815, 14: 0.281, 15: 0.415, 16: 0.246, 17: 0.192, 18: 0.381}
## TESTING Restuls for Model: DeepLab + Loss: BCEWithLogitsLoss2d + predict: T ##
test_loss : 0.02362
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.24899999797344208
Mean_Dice : 0.49799999594688416
Class_IoU : {0: 0.456, 1: 0.212, 2: 0.408, 3: 0.047, 4: 0.059, 5: 0.112, 6: 0.088, 7: 0.13, 8: 0.446, 9: 0.102, 10: 0.404, 11: 0.162, 12: 0.089, 13: 0.401, 14: 0.103, 15: 0.169, 16: 0.136, 17: 0.031, 18: 0.144}
Class_Dice : {0: 0.913, 1: 0.423, 2: 0.817, 3: 0.093, 4: 0.118, 5: 0.223, 6: 0.175, 7: 0.26, 8: 0.892, 9: 0.205, 10: 0.808, 11: 0.324, 12: 0.178, 13: 0.802, 14: 0.206, 15: 0.337, 16: 0.271, 17: 0.062, 18: 0.287}
## TESTING Restuls for Model: DeepLab + Loss: BCEWithLogitsLoss2d + predict: max ##
test_loss : 0.02362
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.22100000083446503
Mean_Dice : 0.44200000166893005
Class_IoU : {0: 0.432, 1: 0.246, 2: 0.395, 3: 0.036, 4: 0.04, 5: 0.13, 6: 0.092, 7: 0.158, 8: 0.439, 9: 0.107, 10: 0.374, 11: 0.175, 12: 0.107, 13: 0.383, 14: 0.059, 15: 0.071, 16: 0.02, 17: 0.038, 18: 0.101}
Class_Dice : {0: 0.864, 1: 0.492, 2: 0.79, 3: 0.073, 4: 0.08, 5: 0.26, 6: 0.184, 7: 0.317, 8: 0.877, 9: 0.214, 10: 0.747, 11: 0.349, 12: 0.214, 13: 0.766, 14: 0.117, 15: 0.143, 16: 0.039, 17: 0.076, 18: 0.201}
## TESTING Restuls for Model: DeepLab + Loss: BCEWithLogitsLoss2d + predict: rankdice ##
test_loss : 0.02362
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.27000001072883606
Mean_Dice : 0.5400000214576721
Class_IoU : {0: 0.459, 1: 0.271, 2: 0.417, 3: 0.066, 4: 0.08, 5: 0.169, 6: 0.11, 7: 0.163, 8: 0.448, 9: 0.127, 10: 0.411, 11: 0.187, 12: 0.113, 13: 0.403, 14: 0.123, 15: 0.174, 16: 0.139, 17: 0.042, 18: 0.161}
Class_Dice : {0: 0.917, 1: 0.542, 2: 0.834, 3: 0.132, 4: 0.159, 5: 0.338, 6: 0.22, 7: 0.325, 8: 0.896, 9: 0.255, 10: 0.821, 11: 0.375, 12: 0.227, 13: 0.806, 14: 0.246, 15: 0.347, 16: 0.279, 17: 0.085, 18: 0.322}
## TESTING Restuls for Model: DeepLab + Loss: BCEDiceLoss + predict: T ##
test_loss : 0.10194
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.20499999821186066
Mean_Dice : 0.4099999964237213
Class_IoU : {0: 0.46, 1: 0.263, 2: 0.418, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.449, 9: 0.0, 10: 0.414, 11: 0.0, 12: 0.0, 13: 0.41, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.92, 1: 0.525, 2: 0.836, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.897, 9: 0.0, 10: 0.829, 11: 0.0, 12: 0.0, 13: 0.82, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: DeepLab + Loss: BCEDiceLoss + predict: max ##
test_loss : 0.10193
Pixel_Accuracy : 0.984000027179718
Mean_IoU : 0.1379999965429306
Mean_Dice : 0.2759999930858612
Class_IoU : {0: 0.452, 1: 0.224, 2: 0.409, 3: 0.0, 4: 0.001, 5: 0.0, 6: 0.0, 7: 0.003, 8: 0.421, 9: 0.001, 10: 0.252, 11: 0.0, 12: 0.0, 13: 0.24, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.904, 1: 0.447, 2: 0.817, 3: 0.0, 4: 0.002, 5: 0.001, 6: 0.0, 7: 0.006, 8: 0.841, 9: 0.003, 10: 0.504, 11: 0.001, 12: 0.0, 13: 0.481, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: DeepLab + Loss: BCEDiceLoss + predict: rankdice ##
test_loss : 0.10194
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.20499999821186066
Mean_Dice : 0.41100001335144043
Class_IoU : {0: 0.46, 1: 0.265, 2: 0.418, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.449, 9: 0.0, 10: 0.415, 11: 0.0, 12: 0.0, 13: 0.41, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.92, 1: 0.53, 2: 0.837, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.897, 9: 0.0, 10: 0.83, 11: 0.0, 12: 0.0, 13: 0.821, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: DeepLab + Loss: BCEDiceLoss + predict: rankdice ##
test_loss : 0.10193
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.20499999821186066
Mean_Dice : 0.41100001335144043
Class_IoU : {0: 0.46, 1: 0.266, 2: 0.418, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.449, 9: 0.0, 10: 0.416, 11: 0.0, 12: 0.0, 13: 0.411, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.92, 1: 0.531, 2: 0.837, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.897, 9: 0.0, 10: 0.831, 11: 0.0, 12: 0.0, 13: 0.822, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: DeepLab + Loss: CE_DiceLoss + predict: max ##
test_loss : 0.26562
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.4880000054836273
Mean_Dice : 0.5659999847412109
Class_IoU : {0: 0.748, 1: 0.496, 2: 0.768, 3: 0.145, 4: 0.114, 5: 0.318, 6: 0.229, 7: 0.361, 8: 0.842, 9: 0.216, 10: 0.786, 11: 0.354, 12: 0.234, 13: 0.767, 14: 0.2, 15: 0.385, 16: 0.15, 17: 0.151, 18: 0.31}
Class_Dice : {0: 0.841, 1: 0.594, 2: 0.849, 3: 0.184, 4: 0.15, 5: 0.455, 6: 0.305, 7: 0.46, 8: 0.904, 9: 0.272, 10: 0.843, 11: 0.444, 12: 0.303, 13: 0.837, 14: 0.229, 15: 0.424, 16: 0.175, 17: 0.19, 18: 0.398}
## TESTING Restuls for Model: DeepLab + Loss: CE_DiceLoss + predict: rankdice ##
test_loss : 0.26562
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.5099999904632568
Mean_Dice : 0.5889999866485596
Class_IoU : {0: 0.748, 1: 0.513, 2: 0.774, 3: 0.164, 4: 0.131, 5: 0.33, 6: 0.251, 7: 0.374, 8: 0.849, 9: 0.227, 10: 0.79, 11: 0.365, 12: 0.251, 13: 0.78, 14: 0.247, 15: 0.447, 16: 0.204, 17: 0.166, 18: 0.337}
Class_Dice : {0: 0.841, 1: 0.611, 2: 0.854, 3: 0.206, 4: 0.172, 5: 0.469, 6: 0.331, 7: 0.475, 8: 0.911, 9: 0.282, 10: 0.848, 11: 0.455, 12: 0.322, 13: 0.848, 14: 0.282, 15: 0.492, 16: 0.236, 17: 0.208, 18: 0.43}
## TESTING Restuls for Model: DeepLab + Loss: DiceLoss + predict: max ##
test_loss : 0.10898
Pixel_Accuracy : 0.9890000224113464
Mean_IoU : 0.3580000102519989
Mean_Dice : 0.39500001072883606
Class_IoU : {0: 0.887, 1: 0.0, 2: 0.726, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.812, 9: 0.0, 10: 0.763, 11: 0.293, 12: 0.0, 13: 0.707, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.933, 1: 0.0, 2: 0.818, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.884, 9: 0.0, 10: 0.823, 11: 0.38, 12: 0.0, 13: 0.79, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: DeepLab + Loss: DiceLoss + predict: T ##
test_loss : 0.10898
Pixel_Accuracy : 0.9890000224113464
Mean_IoU : 0.35899999737739563
Mean_Dice : 0.39500001072883606
Class_IoU : {0: 0.887, 1: 0.0, 2: 0.728, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.813, 9: 0.0, 10: 0.764, 11: 0.295, 12: 0.0, 13: 0.71, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.933, 1: 0.0, 2: 0.82, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.884, 9: 0.0, 10: 0.824, 11: 0.381, 12: 0.0, 13: 0.792, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: DeepLab + Loss: DiceLoss + predict: rankdice ##
test_loss : 0.10898
Pixel_Accuracy : 0.9890000224113464
Mean_IoU : 0.35899999737739563
Mean_Dice : 0.39500001072883606
Class_IoU : {0: 0.887, 1: 0.0, 2: 0.728, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.813, 9: 0.0, 10: 0.765, 11: 0.296, 12: 0.0, 13: 0.709, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.933, 1: 0.0, 2: 0.82, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.884, 9: 0.0, 10: 0.826, 11: 0.382, 12: 0.0, 13: 0.792, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: DeepLab + Loss: LovaszSoftmax + predict: T ##
test_loss : 0.39791
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.47600001096725464
Mean_Dice : 0.5519999861717224
Class_IoU : {0: 0.776, 1: 0.488, 2: 0.733, 3: 0.106, 4: 0.102, 5: 0.316, 6: 0.299, 7: 0.424, 8: 0.838, 9: 0.251, 10: 0.786, 11: 0.388, 12: 0.289, 13: 0.749, 14: 0.139, 15: 0.194, 16: 0.0, 17: 0.134, 18: 0.332}
Class_Dice : {0: 0.86, 1: 0.587, 2: 0.822, 3: 0.139, 4: 0.137, 5: 0.452, 6: 0.385, 7: 0.526, 8: 0.9, 9: 0.312, 10: 0.845, 11: 0.476, 12: 0.368, 13: 0.818, 14: 0.163, 15: 0.221, 16: 0.0, 17: 0.17, 18: 0.422}
## TESTING Restuls for Model: DeepLab + Loss: LovaszSoftmax + predict: max ##
test_loss : 0.39791
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.45100000500679016
Mean_Dice : 0.5230000019073486
Class_IoU : {0: 0.775, 1: 0.487, 2: 0.728, 3: 0.091, 4: 0.087, 5: 0.312, 6: 0.291, 7: 0.423, 8: 0.835, 9: 0.245, 10: 0.782, 11: 0.385, 12: 0.26, 13: 0.74, 14: 0.102, 15: 0.143, 16: 0.0, 17: 0.108, 18: 0.317}
Class_Dice : {0: 0.859, 1: 0.586, 2: 0.819, 3: 0.12, 4: 0.117, 5: 0.449, 6: 0.376, 7: 0.526, 8: 0.897, 9: 0.305, 10: 0.841, 11: 0.475, 12: 0.332, 13: 0.811, 14: 0.121, 15: 0.164, 16: 0.0, 17: 0.138, 18: 0.403}
## TESTING Restuls for Model: DeepLab + Loss: LovaszSoftmax + predict: rankdice ##
test_loss : 0.39791
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.4779999852180481
Mean_Dice : 0.5550000071525574
Class_IoU : {0: 0.776, 1: 0.494, 2: 0.726, 3: 0.116, 4: 0.106, 5: 0.316, 6: 0.3, 7: 0.426, 8: 0.838, 9: 0.259, 10: 0.787, 11: 0.394, 12: 0.294, 13: 0.749, 14: 0.147, 15: 0.2, 16: 0.0, 17: 0.142, 18: 0.335}
Class_Dice : {0: 0.86, 1: 0.594, 2: 0.815, 3: 0.153, 4: 0.143, 5: 0.453, 6: 0.388, 7: 0.529, 8: 0.9, 9: 0.324, 10: 0.846, 11: 0.486, 12: 0.375, 13: 0.82, 14: 0.175, 15: 0.228, 16: 0.0, 17: 0.182, 18: 0.427}
## TESTING Restuls for Model: FCN8 + Loss: DiceLoss + predict: max ##
test_loss : 0.17416
Pixel_Accuracy : 0.9819999933242798
Mean_IoU : 0.24799999594688416
Mean_Dice : 0.28299999237060547
Class_IoU : {0: 0.861, 1: 0.0, 2: 0.575, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.703, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.585, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.918, 1: 0.0, 2: 0.695, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.811, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.694, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: FCN8 + Loss: DiceLoss + predict: T ##
test_loss : 0.17416
Pixel_Accuracy : 0.9819999933242798
Mean_IoU : 0.24799999594688416
Mean_Dice : 0.28299999237060547
Class_IoU : {0: 0.861, 1: 0.0, 2: 0.575, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.703, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.587, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.918, 1: 0.0, 2: 0.695, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.811, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.696, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: FCN8 + Loss: DiceLoss + predict: rankdice ##
test_loss : 0.17416
Pixel_Accuracy : 0.9819999933242798
Mean_IoU : 0.24799999594688416
Mean_Dice : 0.28299999237060547
Class_IoU : {0: 0.861, 1: 0.0, 2: 0.575, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.703, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.587, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.918, 1: 0.0, 2: 0.695, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.811, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.696, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: FCN8 + Loss: CrossEntropyLoss2d + predict: T ##
test_loss : 0.21361
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.43700000643730164
Mean_Dice : 0.5139999985694885
Class_IoU : {0: 0.758, 1: 0.405, 2: 0.72, 3: 0.096, 4: 0.1, 5: 0.133, 6: 0.163, 7: 0.271, 8: 0.812, 9: 0.171, 10: 0.719, 11: 0.268, 12: 0.173, 13: 0.708, 14: 0.166, 15: 0.294, 16: 0.163, 17: 0.083, 18: 0.277}
Class_Dice : {0: 0.848, 1: 0.504, 2: 0.816, 3: 0.126, 4: 0.14, 5: 0.212, 6: 0.224, 7: 0.366, 8: 0.886, 9: 0.223, 10: 0.793, 11: 0.348, 12: 0.232, 13: 0.795, 14: 0.195, 15: 0.336, 16: 0.193, 17: 0.109, 18: 0.366}
## TESTING Restuls for Model: FCN8 + Loss: CrossEntropyLoss2d + predict: max ##
test_loss : 0.21361
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.4259999990463257
Mean_Dice : 0.5049999952316284
Class_IoU : {0: 0.756, 1: 0.425, 2: 0.717, 3: 0.102, 4: 0.098, 5: 0.153, 6: 0.158, 7: 0.279, 8: 0.802, 9: 0.184, 10: 0.721, 11: 0.286, 12: 0.172, 13: 0.681, 14: 0.144, 15: 0.234, 16: 0.115, 17: 0.093, 18: 0.254}
Class_Dice : {0: 0.847, 1: 0.531, 2: 0.811, 3: 0.133, 4: 0.139, 5: 0.244, 6: 0.221, 7: 0.379, 8: 0.878, 9: 0.242, 10: 0.795, 11: 0.374, 12: 0.234, 13: 0.774, 14: 0.172, 15: 0.269, 16: 0.137, 17: 0.121, 18: 0.343}
## TESTING Restuls for Model: FCN8 + Loss: CrossEntropyLoss2d + predict: rankdice ##
test_loss : 0.21361
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.453000009059906
Mean_Dice : 0.5350000262260437
Class_IoU : {0: 0.758, 1: 0.459, 2: 0.728, 3: 0.122, 4: 0.122, 5: 0.176, 6: 0.171, 7: 0.284, 8: 0.813, 9: 0.203, 10: 0.729, 11: 0.3, 12: 0.182, 13: 0.703, 14: 0.181, 15: 0.312, 16: 0.173, 17: 0.108, 18: 0.277}
Class_Dice : {0: 0.848, 1: 0.564, 2: 0.823, 3: 0.155, 4: 0.169, 5: 0.28, 6: 0.238, 7: 0.387, 8: 0.888, 9: 0.262, 10: 0.804, 11: 0.387, 12: 0.243, 13: 0.793, 14: 0.21, 15: 0.354, 16: 0.208, 17: 0.135, 18: 0.37}
## TESTING Restuls for Model: FCN8 + Loss: BCEWithLogitsLoss2d + predict: T ##
test_loss : 0.03307
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.19699999690055847
Mean_Dice : 0.39399999380111694
Class_IoU : {0: 0.449, 1: 0.133, 2: 0.366, 3: 0.025, 4: 0.03, 5: 0.017, 6: 0.023, 7: 0.045, 8: 0.42, 9: 0.06, 10: 0.34, 11: 0.092, 12: 0.041, 13: 0.338, 14: 0.063, 15: 0.106, 16: 0.06, 17: 0.021, 18: 0.076}
Class_Dice : {0: 0.899, 1: 0.266, 2: 0.732, 3: 0.049, 4: 0.06, 5: 0.034, 6: 0.046, 7: 0.09, 8: 0.84, 9: 0.12, 10: 0.679, 11: 0.184, 12: 0.083, 13: 0.677, 14: 0.126, 15: 0.212, 16: 0.12, 17: 0.042, 18: 0.151}
## TESTING Restuls for Model: FCN8 + Loss: BCEWithLogitsLoss2d + predict: max ##
test_loss : 0.03307
Pixel_Accuracy : 0.9829999804496765
Mean_IoU : 0.1979999989271164
Mean_Dice : 0.39500001072883606
Class_IoU : {0: 0.43, 1: 0.174, 2: 0.352, 3: 0.033, 4: 0.03, 5: 0.031, 6: 0.023, 7: 0.059, 8: 0.416, 9: 0.072, 10: 0.343, 11: 0.11, 12: 0.055, 13: 0.341, 14: 0.048, 15: 0.102, 16: 0.051, 17: 0.017, 18: 0.093}
Class_Dice : {0: 0.86, 1: 0.349, 2: 0.704, 3: 0.066, 4: 0.06, 5: 0.063, 6: 0.046, 7: 0.119, 8: 0.832, 9: 0.144, 10: 0.686, 11: 0.219, 12: 0.111, 13: 0.682, 14: 0.097, 15: 0.204, 16: 0.103, 17: 0.033, 18: 0.186}
## TESTING Restuls for Model: FCN8 + Loss: BCEWithLogitsLoss2d + predict: rankdice ##
test_loss : 0.03307
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.20600000023841858
Mean_Dice : 0.4129999876022339
Class_IoU : {0: 0.451, 1: 0.179, 2: 0.38, 3: 0.032, 4: 0.038, 5: 0.023, 6: 0.019, 7: 0.049, 8: 0.42, 9: 0.069, 10: 0.343, 11: 0.105, 12: 0.043, 13: 0.346, 14: 0.072, 15: 0.125, 16: 0.089, 17: 0.027, 18: 0.086}
Class_Dice : {0: 0.902, 1: 0.357, 2: 0.759, 3: 0.065, 4: 0.077, 5: 0.045, 6: 0.039, 7: 0.097, 8: 0.84, 9: 0.138, 10: 0.686, 11: 0.21, 12: 0.087, 13: 0.693, 14: 0.144, 15: 0.251, 16: 0.179, 17: 0.054, 18: 0.172}
## TESTING Restuls for Model: FCN8 + Loss: BCEDiceLoss + predict: T ##
test_loss : 0.16009
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.1459999978542328
Mean_Dice : 0.29100000858306885
Class_IoU : {0: 0.457, 1: 0.0, 2: 0.388, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.426, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.361, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.914, 1: 0.0, 2: 0.777, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.853, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.723, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: FCN8 + Loss: BCEDiceLoss + predict: max ##
test_loss : 0.16009
Pixel_Accuracy : 0.9789999723434448
Mean_IoU : 0.11299999803304672
Mean_Dice : 0.22599999606609344
Class_IoU : {0: 0.442, 1: 0.003, 2: 0.377, 3: 0.002, 4: 0.001, 5: 0.001, 6: 0.0, 7: 0.001, 8: 0.403, 9: 0.003, 10: 0.087, 11: 0.028, 12: 0.0, 13: 0.332, 14: 0.0, 15: 0.001, 16: 0.0, 17: 0.0, 18: 0.006}
Class_Dice : {0: 0.883, 1: 0.007, 2: 0.754, 3: 0.003, 4: 0.001, 5: 0.003, 6: 0.001, 7: 0.001, 8: 0.805, 9: 0.006, 10: 0.174, 11: 0.057, 12: 0.0, 13: 0.665, 14: 0.0, 15: 0.003, 16: 0.0, 17: 0.0, 18: 0.013}
## TESTING Restuls for Model: FCN8 + Loss: BCEDiceLoss + predict: rankdice ##
test_loss : 0.16009
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.1459999978542328
Mean_Dice : 0.29100000858306885
Class_IoU : {0: 0.457, 1: 0.0, 2: 0.389, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.426, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.362, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
Class_Dice : {0: 0.914, 1: 0.0, 2: 0.777, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.853, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.724, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0}
## TESTING Restuls for Model: FCN8 + Loss: FocalLoss + predict: max ##
test_loss : 0.10072
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.41600000858306885
Mean_Dice : 0.4959999918937683
Class_IoU : {0: 0.756, 1: 0.367, 2: 0.708, 3: 0.101, 4: 0.081, 5: 0.154, 6: 0.158, 7: 0.274, 8: 0.792, 9: 0.177, 10: 0.707, 11: 0.28, 12: 0.177, 13: 0.672, 14: 0.151, 15: 0.24, 16: 0.106, 17: 0.102, 18: 0.253}
Class_Dice : {0: 0.847, 1: 0.472, 2: 0.806, 3: 0.132, 4: 0.116, 5: 0.247, 6: 0.223, 7: 0.376, 8: 0.87, 9: 0.234, 10: 0.785, 11: 0.367, 12: 0.244, 13: 0.768, 14: 0.182, 15: 0.28, 16: 0.127, 17: 0.135, 18: 0.344}
## TESTING Restuls for Model: FCN8 + Loss: FocalLoss + predict: T ##
test_loss : 0.10072
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.41200000047683716
Mean_Dice : 0.48500001430511475
Class_IoU : {0: 0.763, 1: 0.289, 2: 0.677, 3: 0.084, 4: 0.08, 5: 0.107, 6: 0.145, 7: 0.248, 8: 0.799, 9: 0.137, 10: 0.695, 11: 0.216, 12: 0.153, 13: 0.698, 14: 0.157, 15: 0.279, 16: 0.175, 17: 0.076, 18: 0.255}
Class_Dice : {0: 0.851, 1: 0.375, 2: 0.781, 3: 0.112, 4: 0.114, 5: 0.173, 6: 0.201, 7: 0.339, 8: 0.876, 9: 0.183, 10: 0.774, 11: 0.283, 12: 0.209, 13: 0.786, 14: 0.183, 15: 0.324, 16: 0.208, 17: 0.102, 18: 0.339}
## TESTING Restuls for Model: FCN8 + Loss: FocalLoss + predict: rankdice ## tuncate mean
test_loss : 0.10072
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.43700000643730164
Mean_Dice : 0.5149999856948853
Class_IoU : {0: 0.762, 1: 0.383, 2: 0.716, 3: 0.11, 4: 0.107, 5: 0.149, 6: 0.16, 7: 0.266, 8: 0.802, 9: 0.172, 10: 0.71, 11: 0.26, 12: 0.175, 13: 0.697, 14: 0.167, 15: 0.3, 16: 0.19, 17: 0.089, 18: 0.272}
Class_Dice : {0: 0.851, 1: 0.477, 2: 0.814, 3: 0.138, 4: 0.149, 5: 0.237, 6: 0.22, 7: 0.363, 8: 0.879, 9: 0.223, 10: 0.787, 11: 0.334, 12: 0.233, 13: 0.788, 14: 0.194, 15: 0.345, 16: 0.221, 17: 0.116, 18: 0.359}
## TESTING Restuls for Model: FCN8 + Loss: FocalLoss + predict: rankdice ##
test_loss : 0.10072
Pixel_Accuracy : 0.984000027179718
Mean_IoU : 0.42899999022483826
Mean_Dice : 0.5070000290870667
Class_IoU : {0: 0.762, 1: 0.386, 2: 0.711, 3: 0.114, 4: 0.106, 5: 0.121, 6: 0.126, 7: 0.243, 8: 0.798, 9: 0.178, 10: 0.7, 11: 0.28, 12: 0.17, 13: 0.682, 14: 0.177, 15: 0.306, 16: 0.186, 17: 0.091, 18: 0.258}
Class_Dice : {0: 0.851, 1: 0.484, 2: 0.81, 3: 0.145, 4: 0.15, 5: 0.201, 6: 0.183, 7: 0.339, 8: 0.876, 9: 0.231, 10: 0.778, 11: 0.359, 12: 0.23, 13: 0.775, 14: 0.209, 15: 0.352, 16: 0.222, 17: 0.118, 18: 0.345}
## TESTING Restuls for Model: FCN8 + Loss: LovaszSoftmax + predict: max ##
test_loss : 0.46925
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.3580000102519989
Mean_Dice : 0.42899999022483826
Class_IoU : {0: 0.757, 1: 0.394, 2: 0.702, 3: 0.075, 4: 0.066, 5: 0.171, 6: 0.151, 7: 0.325, 8: 0.791, 9: 0.197, 10: 0.722, 11: 0.296, 12: 0.151, 13: 0.673, 14: 0.05, 15: 0.107, 16: 0.0, 17: 0.055, 18: 0.262}
Class_Dice : {0: 0.847, 1: 0.5, 2: 0.8, 3: 0.104, 4: 0.094, 5: 0.273, 6: 0.217, 7: 0.437, 8: 0.866, 9: 0.261, 10: 0.797, 11: 0.386, 12: 0.211, 13: 0.764, 14: 0.067, 15: 0.128, 16: 0.0, 17: 0.077, 18: 0.353}
## TESTING Restuls for Model: FCN8 + Loss: LovaszSoftmax + predict: T ##
test_loss : 0.46925
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.40400001406669617
Mean_Dice : 0.48100000619888306
Class_IoU : {0: 0.758, 1: 0.385, 2: 0.7, 3: 0.106, 4: 0.084, 5: 0.169, 6: 0.187, 7: 0.328, 8: 0.795, 9: 0.203, 10: 0.727, 11: 0.302, 12: 0.188, 13: 0.69, 14: 0.084, 15: 0.211, 16: 0.0, 17: 0.086, 18: 0.288}
Class_Dice : {0: 0.848, 1: 0.489, 2: 0.798, 3: 0.142, 4: 0.115, 5: 0.268, 6: 0.263, 7: 0.438, 8: 0.871, 9: 0.267, 10: 0.803, 11: 0.391, 12: 0.257, 13: 0.779, 14: 0.107, 15: 0.249, 16: 0.0, 17: 0.117, 18: 0.383}
## TESTING Restuls for Model: FCN8 + Loss: LovaszSoftmax + predict: rankdice ##
test_loss : 0.46925
Pixel_Accuracy : 0.9850000143051147
Mean_IoU : 0.4090000092983246
Mean_Dice : 0.48899999260902405
Class_IoU : {0: 0.758, 1: 0.408, 2: 0.705, 3: 0.119, 4: 0.088, 5: 0.177, 6: 0.172, 7: 0.332, 8: 0.796, 9: 0.214, 10: 0.729, 11: 0.311, 12: 0.19, 13: 0.695, 14: 0.093, 15: 0.227, 16: 0.0, 17: 0.086, 18: 0.287}
Class_Dice : {0: 0.848, 1: 0.518, 2: 0.803, 3: 0.162, 4: 0.124, 5: 0.284, 6: 0.247, 7: 0.444, 8: 0.872, 9: 0.282, 10: 0.805, 11: 0.403, 12: 0.262, 13: 0.787, 14: 0.121, 15: 0.269, 16: 0.0, 17: 0.119, 18: 0.383}
## TESTING Restuls for Model: PSPNet + Loss: CrossEntropyLoss2d + predict: T ##
test_loss : 0.15925
Pixel_Accuracy : 0.9890000224113464
Mean_IoU : 0.4959999918937683
Mean_Dice : 0.574999988079071
Class_IoU : {0: 0.772, 1: 0.478, 2: 0.762, 3: 0.136, 4: 0.109, 5: 0.29, 6: 0.265, 7: 0.39, 8: 0.841, 9: 0.201, 10: 0.77, 11: 0.363, 12: 0.273, 13: 0.769, 14: 0.219, 15: 0.422, 16: 0.307, 17: 0.158, 18: 0.325}
Class_Dice : {0: 0.857, 1: 0.573, 2: 0.846, 3: 0.174, 4: 0.147, 5: 0.419, 6: 0.349, 7: 0.499, 8: 0.902, 9: 0.257, 10: 0.836, 11: 0.451, 12: 0.351, 13: 0.841, 14: 0.247, 15: 0.468, 16: 0.349, 17: 0.197, 18: 0.414}
## TESTING Restuls for Model: PSPNet + Loss: CrossEntropyLoss2d + predict: max ##
test_loss : 0.15925
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.48500001430511475
Mean_Dice : 0.5649999976158142
Class_IoU : {0: 0.768, 1: 0.489, 2: 0.759, 3: 0.133, 4: 0.099, 5: 0.295, 6: 0.257, 7: 0.387, 8: 0.836, 9: 0.208, 10: 0.769, 11: 0.372, 12: 0.272, 13: 0.751, 14: 0.204, 15: 0.395, 16: 0.268, 17: 0.152, 18: 0.303}
Class_Dice : {0: 0.854, 1: 0.585, 2: 0.844, 3: 0.172, 4: 0.136, 5: 0.428, 6: 0.341, 7: 0.498, 8: 0.9, 9: 0.268, 10: 0.835, 11: 0.464, 12: 0.351, 13: 0.826, 14: 0.233, 15: 0.437, 16: 0.308, 17: 0.193, 18: 0.392}
## TESTING Restuls for Model: PSPNet + Loss: CrossEntropyLoss2d + predict: rankdice ##
test_loss : 0.15925
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.5099999904632568
Mean_Dice : 0.5929999947547913
Class_IoU : {0: 0.771, 1: 0.508, 2: 0.767, 3: 0.164, 4: 0.117, 5: 0.317, 6: 0.283, 7: 0.401, 8: 0.841, 9: 0.231, 10: 0.778, 11: 0.4, 12: 0.292, 13: 0.766, 14: 0.233, 15: 0.465, 16: 0.315, 17: 0.177, 18: 0.326}
Class_Dice : {0: 0.856, 1: 0.608, 2: 0.851, 3: 0.21, 4: 0.158, 5: 0.46, 6: 0.374, 7: 0.514, 8: 0.903, 9: 0.294, 10: 0.845, 11: 0.495, 12: 0.372, 13: 0.84, 14: 0.266, 15: 0.513, 16: 0.358, 17: 0.222, 18: 0.419}
## TESTING Restuls for Model: PSPNet + Loss: BCEWithLogitsLoss2d + predict: T ##
test_loss : 0.02311
Pixel_Accuracy : 0.9919999837875366
Mean_IoU : 0.25699999928474426
Mean_Dice : 0.5139999985694885
Class_IoU : {0: 0.461, 1: 0.219, 2: 0.397, 3: 0.024, 4: 0.076, 5: 0.135, 6: 0.094, 7: 0.176, 8: 0.445, 9: 0.099, 10: 0.4, 11: 0.164, 12: 0.093, 13: 0.404, 14: 0.109, 15: 0.182, 16: 0.169, 17: 0.035, 18: 0.165}
Class_Dice : {0: 0.922, 1: 0.438, 2: 0.794, 3: 0.049, 4: 0.152, 5: 0.271, 6: 0.187, 7: 0.353, 8: 0.89, 9: 0.198, 10: 0.801, 11: 0.328, 12: 0.186, 13: 0.808, 14: 0.218, 15: 0.363, 16: 0.338, 17: 0.07, 18: 0.329}
## TESTING Restuls for Model: PSPNet + Loss: BCEWithLogitsLoss2d + predict: max ##
test_loss : 0.02311
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.23800000548362732
Mean_Dice : 0.47600001096725464
Class_IoU : {0: 0.433, 1: 0.255, 2: 0.403, 3: 0.041, 4: 0.042, 5: 0.161, 6: 0.11, 7: 0.182, 8: 0.441, 9: 0.099, 10: 0.383, 11: 0.173, 12: 0.111, 13: 0.357, 14: 0.064, 15: 0.073, 16: 0.083, 17: 0.05, 18: 0.172}
Class_Dice : {0: 0.867, 1: 0.509, 2: 0.806, 3: 0.082, 4: 0.085, 5: 0.322, 6: 0.22, 7: 0.364, 8: 0.883, 9: 0.199, 10: 0.767, 11: 0.346, 12: 0.222, 13: 0.713, 14: 0.128, 15: 0.146, 16: 0.166, 17: 0.101, 18: 0.344}
## TESTING Restuls for Model: PSPNet + Loss: BCEWithLogitsLoss2d + predict: rankdice ##
test_loss : 0.02311
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.2759999930858612
Mean_Dice : 0.5509999990463257
Class_IoU : {0: 0.461, 1: 0.27, 2: 0.41, 3: 0.042, 4: 0.095, 5: 0.182, 6: 0.106, 7: 0.198, 8: 0.447, 9: 0.127, 10: 0.403, 11: 0.193, 12: 0.12, 13: 0.406, 14: 0.134, 15: 0.196, 16: 0.174, 17: 0.044, 18: 0.184}
Class_Dice : {0: 0.922, 1: 0.54, 2: 0.821, 3: 0.083, 4: 0.191, 5: 0.364, 6: 0.213, 7: 0.396, 8: 0.894, 9: 0.253, 10: 0.807, 11: 0.386, 12: 0.24, 13: 0.812, 14: 0.268, 15: 0.392, 16: 0.347, 17: 0.087, 18: 0.367}
## TESTING Restuls for Model: PSPNet + Loss: DiceLoss + predict: T ##
test_loss : 0.08758
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.4350000023841858
Mean_Dice : 0.4909999966621399
Class_IoU : {0: 0.919, 1: 0.395, 2: 0.754, 3: 0.0, 4: 0.12, 5: 0.0, 6: 0.0, 7: 0.309, 8: 0.834, 9: 0.106, 10: 0.758, 11: 0.317, 12: 0.0, 13: 0.727, 14: 0.0, 15: 0.0, 16: 0.205, 17: 0.0, 18: 0.296}
Class_Dice : {0: 0.951, 1: 0.49, 2: 0.839, 3: 0.0, 4: 0.165, 5: 0.0, 6: 0.0, 7: 0.407, 8: 0.899, 9: 0.143, 10: 0.821, 11: 0.399, 12: 0.0, 13: 0.808, 14: 0.0, 15: 0.0, 16: 0.237, 17: 0.0, 18: 0.394}
## TESTING Restuls for Model: PSPNet + Loss: DiceLoss + predict: max ##
test_loss : 0.08758
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.4320000112056732
Mean_Dice : 0.4869999885559082
Class_IoU : {0: 0.919, 1: 0.395, 2: 0.754, 3: 0.0, 4: 0.112, 5: 0.0, 6: 0.0, 7: 0.309, 8: 0.834, 9: 0.104, 10: 0.756, 11: 0.313, 12: 0.0, 13: 0.723, 14: 0.0, 15: 0.0, 16: 0.175, 17: 0.0, 18: 0.292}
Class_Dice : {0: 0.951, 1: 0.49, 2: 0.839, 3: 0.0, 4: 0.154, 5: 0.0, 6: 0.0, 7: 0.406, 8: 0.899, 9: 0.142, 10: 0.819, 11: 0.395, 12: 0.0, 13: 0.805, 14: 0.0, 15: 0.0, 16: 0.202, 17: 0.0, 18: 0.389}
## TESTING Restuls for Model: PSPNet + Loss: DiceLoss + predict: rankdice ##
test_loss : 0.08758
Pixel_Accuracy : 0.9909999966621399
Mean_IoU : 0.4359999895095825
Mean_Dice : 0.49300000071525574
Class_IoU : {0: 0.919, 1: 0.4, 2: 0.754, 3: 0.0, 4: 0.121, 5: 0.0, 6: 0.0, 7: 0.312, 8: 0.835, 9: 0.114, 10: 0.759, 11: 0.319, 12: 0.0, 13: 0.726, 14: 0.0, 15: 0.0, 16: 0.206, 17: 0.0, 18: 0.298}
Class_Dice : {0: 0.951, 1: 0.496, 2: 0.839, 3: 0.0, 4: 0.167, 5: 0.0, 6: 0.0, 7: 0.411, 8: 0.899, 9: 0.154, 10: 0.821, 11: 0.401, 12: 0.0, 13: 0.808, 14: 0.0, 15: 0.0, 16: 0.238, 17: 0.0, 18: 0.396}
## TESTING Restuls for Model: PSPNet + Loss: BCEDiceLoss + predict: T ##
test_loss : 0.08892
Pixel_Accuracy : 0.9919999837875366
Mean_IoU : 0.23100000619888306
Mean_Dice : 0.46299999952316284
Class_IoU : {0: 0.467, 1: 0.255, 2: 0.418, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.448, 9: 0.117, 10: 0.41, 11: 0.189, 12: 0.0, 13: 0.414, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.19}
Class_Dice : {0: 0.934, 1: 0.51, 2: 0.836, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.897, 9: 0.235, 10: 0.82, 11: 0.377, 12: 0.0, 13: 0.829, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.38}
## TESTING Restuls for Model: PSPNet + Loss: BCEDiceLoss + predict: max ##
test_loss : 0.08892
Pixel_Accuracy : 0.9860000014305115
Mean_IoU : 0.164000004529953
Mean_Dice : 0.3269999921321869
Class_IoU : {0: 0.466, 1: 0.275, 2: 0.403, 3: 0.001, 4: 0.004, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.434, 9: 0.062, 10: 0.337, 11: 0.143, 12: 0.0, 13: 0.276, 14: 0.0, 15: 0.0, 16: 0.001, 17: 0.0, 18: 0.052}
Class_Dice : {0: 0.931, 1: 0.549, 2: 0.806, 3: 0.001, 4: 0.009, 5: 0.001, 6: 0.001, 7: 0.0, 8: 0.868, 9: 0.124, 10: 0.674, 11: 0.286, 12: 0.0, 13: 0.552, 14: 0.0, 15: 0.0, 16: 0.001, 17: 0.0, 18: 0.105}
## TESTING Restuls for Model: PSPNet + Loss: BCEDiceLoss + predict: rankdice ##
test_loss : 0.08892
Pixel_Accuracy : 0.9919999837875366
Mean_IoU : 0.23100000619888306
Mean_Dice : 0.4620000123977661
Class_IoU : {0: 0.468, 1: 0.259, 2: 0.415, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.448, 9: 0.123, 10: 0.411, 11: 0.192, 12: 0.0, 13: 0.415, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.196}
Class_Dice : {0: 0.936, 1: 0.517, 2: 0.831, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.897, 9: 0.245, 10: 0.822, 11: 0.384, 12: 0.0, 13: 0.829, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.393}
## TESTING Restuls for Model: PSPNet + Loss: FocalLoss + predict: T ##
test_loss : 0.06833
Pixel_Accuracy : 0.9890000224113464
Mean_IoU : 0.4819999933242798
Mean_Dice : 0.5600000023841858
Class_IoU : {0: 0.779, 1: 0.438, 2: 0.748, 3: 0.124, 4: 0.089, 5: 0.234, 6: 0.24, 7: 0.351, 8: 0.838, 9: 0.185, 10: 0.752, 11: 0.341, 12: 0.24, 13: 0.762, 14: 0.227, 15: 0.438, 16: 0.31, 17: 0.161, 18: 0.329}
Class_Dice : {0: 0.861, 1: 0.536, 2: 0.834, 3: 0.161, 4: 0.124, 5: 0.347, 6: 0.315, 7: 0.459, 8: 0.902, 9: 0.241, 10: 0.82, 11: 0.426, 12: 0.312, 13: 0.834, 14: 0.256, 15: 0.488, 16: 0.363, 17: 0.204, 18: 0.421}
## TESTING Restuls for Model: PSPNet + Loss: FocalLoss + predict: max ##
test_loss : 0.06833
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.47699999809265137
Mean_Dice : 0.5580000281333923
Class_IoU : {0: 0.77, 1: 0.472, 2: 0.746, 3: 0.135, 4: 0.08, 5: 0.271, 6: 0.241, 7: 0.373, 8: 0.83, 9: 0.214, 10: 0.752, 11: 0.368, 12: 0.259, 13: 0.74, 14: 0.202, 15: 0.373, 16: 0.248, 17: 0.146, 18: 0.317}
Class_Dice : {0: 0.856, 1: 0.574, 2: 0.833, 3: 0.177, 4: 0.112, 5: 0.399, 6: 0.322, 7: 0.487, 8: 0.896, 9: 0.278, 10: 0.82, 11: 0.464, 12: 0.336, 13: 0.818, 14: 0.23, 15: 0.412, 16: 0.287, 17: 0.186, 18: 0.411}
## TESTING Restuls for Model: PSPNet + Loss: FocalLoss + predict: rankdice ## truncate mean
test_loss : 0.06833
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.5
Mean_Dice : 0.5820000171661377
Class_IoU : {0: 0.777, 1: 0.478, 2: 0.756, 3: 0.15, 4: 0.1, 5: 0.287, 6: 0.258, 7: 0.38, 8: 0.832, 9: 0.222, 10: 0.766, 11: 0.369, 12: 0.265, 13: 0.756, 14: 0.236, 15: 0.462, 16: 0.327, 17: 0.173, 18: 0.345}
Class_Dice : {0: 0.86, 1: 0.58, 2: 0.843, 3: 0.194, 4: 0.138, 5: 0.42, 6: 0.341, 7: 0.494, 8: 0.899, 9: 0.282, 10: 0.834, 11: 0.459, 12: 0.341, 13: 0.831, 14: 0.265, 15: 0.51, 16: 0.377, 17: 0.215, 18: 0.439}
## TESTING Restuls for Model: PSPNet + Loss: FocalLoss + predict: rankdice ##
test_loss : 0.06833
Pixel_Accuracy : 0.9869999885559082
Mean_IoU : 0.4909999966621399
Mean_Dice : 0.5740000009536743
Class_IoU : {0: 0.777, 1: 0.484, 2: 0.747, 3: 0.168, 4: 0.098, 5: 0.231, 6: 0.247, 7: 0.351, 8: 0.828, 9: 0.227, 10: 0.762, 11: 0.382, 12: 0.263, 13: 0.74, 14: 0.248, 15: 0.468, 16: 0.322, 17: 0.168, 18: 0.325}
Class_Dice : {0: 0.86, 1: 0.587, 2: 0.836, 3: 0.215, 4: 0.137, 5: 0.356, 6: 0.335, 7: 0.466, 8: 0.896, 9: 0.287, 10: 0.831, 11: 0.476, 12: 0.339, 13: 0.818, 14: 0.281, 15: 0.515, 16: 0.373, 17: 0.211, 18: 0.42}
## TESTING Restuls for Model: PSPNet + Loss: LovaszSoftmax + predict: T ##
test_loss : 0.38133
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.48899999260902405
Mean_Dice : 0.5680000185966492
Class_IoU : {0: 0.77, 1: 0.488, 2: 0.75, 3: 0.131, 4: 0.094, 5: 0.323, 6: 0.358, 7: 0.473, 8: 0.836, 9: 0.234, 10: 0.784, 11: 0.422, 12: 0.302, 13: 0.747, 14: 0.153, 15: 0.297, 16: 0.089, 17: 0.154, 18: 0.382}
Class_Dice : {0: 0.856, 1: 0.587, 2: 0.836, 3: 0.171, 4: 0.128, 5: 0.461, 6: 0.456, 7: 0.585, 8: 0.9, 9: 0.3, 10: 0.847, 11: 0.518, 12: 0.382, 13: 0.818, 14: 0.177, 15: 0.325, 16: 0.106, 17: 0.196, 18: 0.48}
## TESTING Restuls for Model: PSPNet + Loss: LovaszSoftmax + predict: max ##
test_loss : 0.38133
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.47699999809265137
Mean_Dice : 0.5540000200271606
Class_IoU : {0: 0.769, 1: 0.488, 2: 0.748, 3: 0.119, 4: 0.092, 5: 0.322, 6: 0.356, 7: 0.472, 8: 0.833, 9: 0.231, 10: 0.784, 11: 0.421, 12: 0.291, 13: 0.743, 14: 0.136, 15: 0.256, 16: 0.071, 17: 0.139, 18: 0.373}
Class_Dice : {0: 0.855, 1: 0.588, 2: 0.834, 3: 0.155, 4: 0.125, 5: 0.46, 6: 0.454, 7: 0.584, 8: 0.897, 9: 0.297, 10: 0.847, 11: 0.518, 12: 0.37, 13: 0.815, 14: 0.157, 15: 0.281, 16: 0.085, 17: 0.178, 18: 0.469}
## TESTING Restuls for Model: PSPNet + Loss: LovaszSoftmax + predict: rankdice ##
test_loss : 0.38133
Pixel_Accuracy : 0.9879999756813049
Mean_IoU : 0.4909999966621399
Mean_Dice : 0.5699999928474426
Class_IoU : {0: 0.77, 1: 0.491, 2: 0.751, 3: 0.136, 4: 0.096, 5: 0.325, 6: 0.358, 7: 0.474, 8: 0.836, 9: 0.24, 10: 0.785, 11: 0.425, 12: 0.303, 13: 0.747, 14: 0.154, 15: 0.3, 16: 0.091, 17: 0.157, 18: 0.383}
Class_Dice : {0: 0.856, 1: 0.591, 2: 0.837, 3: 0.177, 4: 0.131, 5: 0.464, 6: 0.456, 7: 0.586, 8: 0.9, 9: 0.308, 10: 0.848, 11: 0.523, 12: 0.383, 13: 0.818, 14: 0.179, 15: 0.328, 16: 0.11, 17: 0.2, 18: 0.482}
TESTING Results for kvasirSEG dataset - TESTING Restuls for Model: PSPNet + Loss: CrossEntropyLoss2d + predict: T + temperature: 1.00 -
test_loss : 0.1149
Pixel_Accuracy : 0.9599999785423279
Mean_IoU : 0.7919999957084656
Mean_Dice : 0.8629999756813049
Class_IoU : {0: 0.951, 1: 0.792}
Class_Dice : {0: 0.973, 1: 0.863}
- TESTING Restuls for Model: PSPNet + Loss: CrossEntropyLoss2d + predict: rankdice + temperature: 1.00 -
test_loss : 0.1149
Pixel_Accuracy : 0.9559999704360962
Mean_IoU : 0.7979999780654907
Mean_Dice : 0.8709999918937683
Class_IoU : {0: 0.941, 1: 0.798}
Class_Dice : {0: 0.963, 1: 0.871}
- TESTING Restuls for Model: PSPNet + Loss: DiceLoss + predict: T + temperature: 1.00 -
test_loss : 0.04352
Pixel_Accuracy : 0.9580000042915344
Mean_IoU : 0.7590000033378601
Mean_Dice : 0.8349999785423279
Class_IoU : {0: 0.949, 1: 0.759}
Class_Dice : {0: 0.972, 1: 0.835}
- TESTING Restuls for Model: PSPNet + Loss: DiceLoss + predict: rankdice + temperature: 1.00 -
test_loss : 0.04351
Pixel_Accuracy : 0.9380000233650208
Mean_IoU : 0.7609999775886536
Mean_Dice : 0.8370000123977661
Class_IoU : {0: 0.901, 1: 0.761}
Class_Dice : {0: 0.923, 1: 0.837}
- TESTING Restuls for Model: PSPNet + Loss: FocalLoss + predict: T + temperature: 1.00 -
test_loss : 0.02794
Pixel_Accuracy : 0.9589999914169312
Mean_IoU : 0.7540000081062317
Mean_Dice : 0.8379999995231628
Class_IoU : {0: 0.949, 1: 0.754}
Class_Dice : {0: 0.973, 1: 0.838}
- TESTING Restuls for Model: PSPNet + Loss: FocalLoss + predict: rankdice + temperature: 1.00 -
test_loss : 0.02794
Pixel_Accuracy : 0.9480000138282776
Mean_IoU : 0.7239999771118164
Mean_Dice : 0.8180000185966492
Class_IoU : {0: 0.945, 1: 0.724}
Class_Dice : {0: 0.971, 1: 0.818}
- TESTING Restuls for Model: PSPNet + Loss: LovaszSoftmax + predict: T + temperature: 1.00 -
test_loss : 0.14043
Pixel_Accuracy : 0.9580000042915344
Mean_IoU : 0.7919999957084656
Mean_Dice : 0.8600000143051147
Class_IoU : {0: 0.949, 1: 0.792}
Class_Dice : {0: 0.972, 1: 0.86}
- TESTING Restuls for Model: PSPNet + Loss: LovaszSoftmax + predict: rankdice + temperature: 1.00 -
test_loss : 0.14043
Pixel_Accuracy : 0.953000009059906
Mean_IoU : 0.7919999957084656
Mean_Dice : 0.8600000143051147
Class_IoU : {0: 0.94, 1: 0.792}
Class_Dice : {0: 0.962, 1: 0.86}
- TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: T + temperature: 1.00 -
test_loss : 0.11959
Pixel_Accuracy : 0.9620000123977661
Mean_IoU : 0.8069999814033508
Mean_Dice : 0.8790000081062317
Class_IoU : {0: 0.953, 1: 0.807}
Class_Dice : {0: 0.975, 1: 0.879}
- TESTING Restuls for Model: DeepLab + Loss: CrossEntropyLoss2d + predict: rankdice + temperature: 1.00 -
test_loss : 0.11959
Pixel_Accuracy : 0.9620000123977661
Mean_IoU : 0.8090000152587891
Mean_Dice : 0.8830000162124634
Class_IoU : {0: 0.954, 1: 0.809}
Class_Dice : {0: 0.975, 1: 0.883}
- TESTING Restuls for Model: DeepLab + Loss: DiceLoss + predict: T + temperature: 1.00 -
test_loss : 0.04208
Pixel_Accuracy : 0.9580000042915344
Mean_IoU : 0.777999997138977
Mean_Dice : 0.8569999933242798
Class_IoU : {0: 0.948, 1: 0.778}
Class_Dice : {0: 0.972, 1: 0.857}
- TESTING Restuls for Model: DeepLab + Loss: DiceLoss + predict: rankdice + temperature: 1.00 -
test_loss : 0.04208
Pixel_Accuracy : 0.9549999833106995
Mean_IoU : 0.7789999842643738
Mean_Dice : 0.8579999804496765
Class_IoU : {0: 0.939, 1: 0.779}
Class_Dice : {0: 0.962, 1: 0.858}
- TESTING Restuls for Model: DeepLab + Loss: FocalLoss + predict: T + temperature: 1.00 -
test_loss : 0.03236
Pixel_Accuracy : 0.9589999914169312
Mean_IoU : 0.7829999923706055
Mean_Dice : 0.8650000095367432
Class_IoU : {0: 0.949, 1: 0.783}
Class_Dice : {0: 0.972, 1: 0.865}
- TESTING Restuls for Model: DeepLab + Loss: FocalLoss + predict: rankdice + temperature: 1.00 -
test_loss : 0.03236
Pixel_Accuracy : 0.9509999752044678
Mean_IoU : 0.7319999933242798
Mean_Dice : 0.8309999704360962
Class_IoU : {0: 0.95, 1: 0.732}
Class_Dice : {0: 0.973, 1: 0.831}
- TESTING Restuls for Model: DeepLab + Loss: LovaszSoftmax + predict: T + temperature: 1.00 -
test_loss : 0.14522
Pixel_Accuracy : 0.9559999704360962
Mean_IoU : 0.7730000019073486
Mean_Dice : 0.8429999947547913
Class_IoU : {0: 0.947, 1: 0.773}
Class_Dice : {0: 0.971, 1: 0.843}
- TESTING Restuls for Model: DeepLab + Loss: LovaszSoftmax + predict: rankdice + temperature: 1.00 -
test_loss : 0.14522
Pixel_Accuracy : 0.9559999704360962
Mean_IoU : 0.7739999890327454
Mean_Dice : 0.8450000286102295
Class_IoU : {0: 0.947, 1: 0.774}
Class_Dice : {0: 0.971, 1: 0.845}
- TESTING Restuls for Model: FCN8 + Loss: CrossEntropyLoss2d + predict: T + temperature: 1.00 -
test_loss : 0.12674
Pixel_Accuracy : 0.9539999961853027
Mean_IoU : 0.7350000143051147
Mean_Dice : 0.8190000057220459
Class_IoU : {0: 0.944, 1: 0.735}
Class_Dice : {0: 0.97, 1: 0.819}
- TESTING Restuls for Model: FCN8 + Loss: CrossEntropyLoss2d + predict: rankdice + temperature: 1.00 -
test_loss : 0.12674
Pixel_Accuracy : 0.9539999961853027
Mean_IoU : 0.7360000014305115
Mean_Dice : 0.8209999799728394
Class_IoU : {0: 0.944, 1: 0.736}
Class_Dice : {0: 0.97, 1: 0.821}
- TESTING Restuls for Model: FCN8 + Loss: FocalLoss + predict: T + temperature: 1.00 -
test_loss : 0.0356
Pixel_Accuracy : 0.9470000267028809
Mean_IoU : 0.6899999976158142
Mean_Dice : 0.7850000262260437
Class_IoU : {0: 0.935, 1: 0.69}
Class_Dice : {0: 0.965, 1: 0.785}
- TESTING Restuls for Model: FCN8 + Loss: FocalLoss + predict: rankdice + temperature: 1.00 -
test_loss : 0.0356
Pixel_Accuracy : 0.9200000166893005
Mean_IoU : 0.5830000042915344
Mean_Dice : 0.703000009059906
Class_IoU : {0: 0.936, 1: 0.583}
Class_Dice : {0: 0.966, 1: 0.703}
- TESTING Restuls for Model: FCN8 + Loss: LovaszSoftmax + predict: T + temperature: 1.00 -
test_loss : 0.15023
Pixel_Accuracy : 0.953000009059906
Mean_IoU : 0.734000027179718
Mean_Dice : 0.8199999928474426
Class_IoU : {0: 0.941, 1: 0.734}
Class_Dice : {0: 0.968, 1: 0.82}
- TESTING Restuls for Model: FCN8 + Loss: LovaszSoftmax + predict: rankdice + temperature: 1.00 -
test_loss : 0.15023
Pixel_Accuracy : 0.9480000138282776
Mean_IoU : 0.734000027179718
Mean_Dice : 0.8199999928474426
Class_IoU : {0: 0.932, 1: 0.734}
Class_Dice : {0: 0.959, 1: 0.82}