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1.79769e+308 diff --git a/img/test1_stage3.jpg b/img/test1_stage3.jpg index 23bf52a..d138770 100644 Binary files a/img/test1_stage3.jpg and b/img/test1_stage3.jpg differ diff --git a/img/test1_stage4.jpg b/img/test1_stage4.jpg index 9085552..7a5421e 100644 Binary files a/img/test1_stage4.jpg and b/img/test1_stage4.jpg differ diff --git a/img/test2_stage3.jpg b/img/test2_stage3.jpg index 1f99296..d19c161 100644 Binary files a/img/test2_stage3.jpg and b/img/test2_stage3.jpg differ diff --git a/img/test2_stage4.jpg b/img/test2_stage4.jpg index a268107..50e8d76 100644 Binary files a/img/test2_stage4.jpg and b/img/test2_stage4.jpg differ diff --git a/img/test3_stage3.jpg b/img/test3_stage3.jpg index 78de250..57a1bd4 100644 Binary files a/img/test3_stage3.jpg and b/img/test3_stage3.jpg differ diff --git a/img/test3_stage4.jpg b/img/test3_stage4.jpg index 0216bd9..931f8b5 100644 Binary files a/img/test3_stage4.jpg and b/img/test3_stage4.jpg differ diff --git a/model/o.caffemodel b/model/o.caffemodel index 4a956c6..0ee6507 100644 Binary files a/model/o.caffemodel and b/model/o.caffemodel differ diff --git a/mxnet/model.py b/mxnet/model.py index c267306..a677d49 100644 --- a/mxnet/model.py +++ b/mxnet/model.py @@ -51,7 +51,7 @@ def onet(): prelu2 = mx.sym.LeakyReLU(data=conv2, act_type='prelu', name='prelu2') pool2 = mx.sym.Pooling(data=prelu2, kernel=(3, 3), stride=(2, 2), pool_type='max', \ pooling_convention='full', name='pool2') - conv3 = mx.sym.Convolution(data=pool2, kernel=(2, 2), num_filter=64, name='conv3') + conv3 = mx.sym.Convolution(data=pool2, kernel=(3, 3), num_filter=64, name='conv3') prelu3 = mx.sym.LeakyReLU(data=conv3, act_type='prelu', name='prelu3') pool3 = mx.sym.Pooling(data=prelu3, kernel=(2, 2), stride=(2, 2), pool_type='max', \ pooling_convention='full', name='pool3') diff --git a/mxnet/tmp/.gitkeep b/mxnet/tmp/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/proto/o.prototxt b/proto/o.prototxt index 7300317..a28c761 100644 --- a/proto/o.prototxt +++ b/proto/o.prototxt @@ -78,7 +78,7 @@ layer { top: "conv3" convolution_param { num_output: 64 - kernel_size: 2 + kernel_size: 3 stride: 1 } } diff --git a/proto/o_train_val.prototxt b/proto/o_train_val.prototxt index daea42b..8439c6c 100644 --- a/proto/o_train_val.prototxt +++ b/proto/o_train_val.prototxt @@ -132,7 +132,7 @@ layer { } convolution_param { num_output: 64 - kernel_size: 2 + kernel_size: 3 stride: 1 weight_filler { type: "xavier" diff --git a/summary.md b/summary.md index 34f22b2..ceb8b0c 100644 --- a/summary.md +++ b/summary.md @@ -46,7 +46,7 @@ PNet 训练时的模型最好能够保证正样本的准确率 > 80%，负样本 RNet 训练时的模型最好能够保证正样本的准确率 > 93%，负样本的准确率 > 99%，face cls loss 在 0.04 附近。 -RNet 训练时的模型最好能够保证正样本的准确率 > 95% 以保证最后的 recall，负样本的准确率 > 9?%，face cls loss 在 ??? 附近。 +ONet 训练时的模型最好能够保证正样本的准确率 > 95.5% 以保证最后的 recall，负样本的准确率 > 95.5%，face cls loss 在 0.099 附近，landmark reg loss < 0.0008。ONet 本身由于训练数据约束，比较难训练。 具体训练参数查看 train.sh 里的配置。 diff --git a/train.sh b/train.sh index dc60896..5b6e205 100644 --- a/train.sh +++ b/train.sh @@ -18,7 +18,7 @@ python jfda/train.py --net r --gpu $GPU --size 128 --lr 0.05 --lrw 0.1 --lrp 5 - echo "Generate Data for oNet" python jfda/prepare.py --net o --gpu $GPU --detect --celeba --wider --worker 4 echo "Train oNet" -python jfda/train.py --net o --gpu $GPU --size 64 --lr 0.05 --lrw 0.1 --lrp 5 --wd 0.0001 --epoch 25 +python jfda/train.py --net o --gpu $GPU --size 64 --lr 0.05 --lrw 0.1 --lrp 7 --wd 0.0001 --epoch 35 # lnet echo "Generate Data for lNet"