水果、蔬菜分类数据集的28图像识别项目：基于VGG16网络的迁移学习

[train hyper-parameters: Namespace(batch_size=16, lr=0.001, lrf=0.01)] [epoch: 1] train loss:0.0825 test accuracy:0.8349 [epoch: 2] train loss:0.0335 test accuracy:0.8557 [epoch: 3] train loss:0.0242 test accuracy:0.8899 [epoch: 4] train loss:0.0179 test accuracy:0.9083 [epoch: 5] train loss:0.0141 test accuracy:0.8985 [epoch: 6] train loss:0.0106 test accuracy:0.8905 [epoch: 7] train loss:0.0093 test accuracy:0.8813 [epoch: 8] train loss:0.0073 test accuracy:0.9125 [epoch: 9] train loss:0.0063 test accuracy:0.8911 [epoch: 10] train loss:0.0059 test accuracy:0.9168 [epoch: 11] train loss:0.0039 test accuracy:0.9021 [epoch: 12] train loss:0.0042 test accuracy:0.9187 [epoch: 13] train loss:0.0029 test accuracy:0.8972 [epoch: 14] train loss:0.0038 test accuracy:0.9101 [epoch: 15] train loss:0.0038 test accuracy:0.9150 [epoch: 16] train loss:0.0023 test accuracy:0.9070 [epoch: 17] train loss:0.0022 test accuracy:0.9070 [epoch: 18] train loss:0.0012 test accuracy:0.9278 [epoch: 19] train loss:0.0020 test accuracy:0.9162 [epoch: 20] train loss:0.0012 test accuracy:0.9223 [epoch: 21] train loss:0.0010 test accuracy:0.9138 [epoch: 22] train loss:0.0015 test accuracy:0.9040 [epoch: 23] train loss:0.0006 test accuracy:0.9187 [epoch: 24] train loss:0.0007 test accuracy:0.9131 [epoch: 25] train loss:0.0020 test accuracy:0.9064 [epoch: 26] train loss:0.0007 test accuracy:0.9211 [epoch: 27] train loss:0.0005 test accuracy:0.9242 [epoch: 28] train loss:0.0004 test accuracy:0.9229 [epoch: 29] train loss:0.0004 test accuracy:0.9321 [epoch: 30] train loss:0.0002 test accuracy:0.9339 [epoch: 31] train loss:0.0004 test accuracy:0.9266 [epoch: 32] train loss:0.0002 test accuracy:0.9315 [epoch: 33] train loss:0.0003 test accuracy:0.9309 [epoch: 34] train loss:0.0002 test accuracy:0.9291 [epoch: 35] train loss:0.0002 test accuracy:0.9315 [epoch: 36] train loss:0.0001 test accuracy:0.9370 [epoch: 37] train loss:0.0002 test accuracy:0.9346 [epoch: 38] train loss:0.0002 test accuracy:0.9358 [epoch: 39] train loss:0.0002 test accuracy:0.9358 [epoch: 40] train loss:0.0002 test accuracy:0.9346 [epoch: 41] train loss:0.0016 test accuracy:0.9119 [epoch: 42] train loss:0.0011 test accuracy:0.9266 [epoch: 43] train loss:0.0002 test accuracy:0.9321 [epoch: 44] train loss:0.0004 test accuracy:0.9284 [epoch: 45] train loss:0.0002 test accuracy:0.9187 [epoch: 46] train loss:0.0002 test accuracy:0.9346 [epoch: 47] train loss:0.0002 test accuracy:0.9346 [epoch: 48] train loss:0.0001 test accuracy:0.9315 [epoch: 49] train loss:0.0001 test accuracy:0.9321 [epoch: 50] train loss:0.0001 test accuracy:0.9309 [epoch: 51] train loss:0.0001 test accuracy:0.9327 [epoch: 52] train loss:0.0001 test accuracy:0.9364 [epoch: 53] train loss:0.0001 test accuracy:0.9333 [epoch: 54] train loss:0.0001 test accuracy:0.9333 [epoch: 55] train loss:0.0001 test accuracy:0.9352 [epoch: 56] train loss:0.0001 test accuracy:0.9339 [epoch: 57] train loss:0.0001 test accuracy:0.9315 [epoch: 58] train loss:0.0001 test accuracy:0.9352 [epoch: 59] train loss:0.0001 test accuracy:0.9352 [epoch: 60] train loss:0.0001 test accuracy:0.9352 [epoch: 61] train loss:0.0001 test accuracy:0.9339 [epoch: 62] train loss:0.0001 test accuracy:0.9346 [epoch: 63] train loss:0.0001 test accuracy:0.9333 [epoch: 64] train loss:0.0001 test accuracy:0.9333 [epoch: 65] train loss:0.0001 test accuracy:0.9321 [epoch: 66] train loss:0.0001 test accuracy:0.9327 [epoch: 67] train loss:0.0001 test accuracy:0.9321 [epoch: 68] train loss:0.0001 test accuracy:0.9370 [epoch: 69] train loss:0.0001 test accuracy:0.9358 [epoch: 70] train loss:0.0001 test accuracy:0.9364 [epoch: 71] train loss:0.0001 test accuracy:0.9358 [epoch: 72] train loss:0.0001 test accuracy:0.9394 [epoch: 73] train loss:0.0001 test accuracy:0.9413 [epoch: 74] train loss:0.0001 test accuracy:0.9394 [epoch: 75] train loss:0.0001 test accuracy:0.9401 [epoch: 76] train loss:0.0001 test accuracy:0.9407 [epoch: 77] train loss:0.0001 test accuracy:0.9394 [epoch: 78] train loss:0.0001 test accuracy:0.9382 [epoch: 79] train loss:0.0001 test accuracy:0.9388 [epoch: 80] train loss:0.0001 test accuracy:0.9382 [epoch: 81] train loss:0.0001 test accuracy:0.9382 [epoch: 82] train loss:0.0001 test accuracy:0.9388 [epoch: 83] train loss:0.0001 test accuracy:0.9382 [epoch: 84] train loss:0.0001 test accuracy:0.9382 [epoch: 85] train loss:0.0001 test accuracy:0.9382 [epoch: 86] train loss:0.0001 test accuracy:0.9382 [epoch: 87] train loss:0.0001 test accuracy:0.9376 [epoch: 88] train loss:0.0001 test accuracy:0.9382 [epoch: 89] train loss:0.0000 test accuracy:0.9382 [epoch: 90] train loss:0.0001 test accuracy:0.9382 [epoch: 91] train loss:0.0001 test accuracy:0.9382 [epoch: 92] train loss:0.0001 test accuracy:0.9382 [epoch: 93] train loss:0.0001 test accuracy:0.9382 [epoch: 94] train loss:0.0001 test accuracy:0.9382 [epoch: 95] train loss:0.0001 test accuracy:0.9382 [epoch: 96] train loss:0.0001 test accuracy:0.9382 [epoch: 97] train loss:0.0001 test accuracy:0.9382 [epoch: 98] train loss:0.0001 test accuracy:0.9382 [epoch: 99] train loss:0.0001 test accuracy:0.9382 [epoch: 100] train loss:0.0000 test accuracy:0.9382