_C.w_train_binary_txt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/data/binary_label/white_train.txt' _C.w_val_binary_txt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/data/binary_label/white_val.txt' #--ckpt path _C.binary_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/binary_keys_epoch_25_Acc_0.976.pth' _C.pos_binary_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/with_pos_keys_epoch_28_Acc_0.978.pth' #----训练的时候要在tensorboard上面观察loss和Acc的变化,可能过拟合或者学习率过大,需要调整学习率 #---较好的模型应该是lr=0.001,然后lr_decay_in_epoch=20训练得到的 # _C.time_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/time_keys_epoch_25_Acc_0.985.pth' #--这个是lr=0.01,然后lr_decay_in_epoch=5训练得到的,每隔5个epoch学习率下降 _C.time_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/time_keys_epoch_18_Acc_0.982.pth' # _C.time_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/time_keys_epoch_26_Acc_0.982.pth' _C.time_pos_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/time_with_pos_keys_epoch_25_Acc_0.981.pth' #---二分类时的正负样本权重(正样本较少) _C.ALPHA = {'white': 3.0, 'black': 2.0} _C.pos_wieghts = [3, 1, 6] _C.binary_input_size = [112, 32] #---(h,w) _C.neg_pos_ratio = 2 #---负样本/正样本 _C.neg_img_selece_ratio = 0.7 #---自己选取的负样本/当前帧其他的负样本 #---用以训练单个按键是否被按下 _C.One_key_SAVE_IMG_DIR = '/home/lj/cy/data/piano/new/saved/YouTube/train' #---用以测试模型的视频图像存储路径 _C.Test_Key_Dir = '/home/lj/cy/data/piano/new/saved/YouTube/' _C.Test_video = ['level_4_no_02', 'level_2_no_02', 'level_1_no_02', '25', '26'] #---label path(训练二分类) _C.binary_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/data/binary_label'
_C.Own_WHITE_TRAIN_FILE = '/home/data/lj/Piano/KEY_PRESS/data/owndataset/white_train_add_network.npy.npz' _C.Own_WHITE_VAL_FILE = '/home/data/lj/Piano/KEY_PRESS/data/owndataset/white_val.npy.npz' _C.Own_BLACK_TRAIN_FILE = '/home/data/lj/Piano/KEY_PRESS/data/owndataset/black_train_add_network.npy.npz' _C.Own_BLACK_VAL_FILE = '/home/data/lj/Piano/KEY_PRESS/data/owndataset/black_val.npy.npz' _C.SAVE_MODEL_DIR = '/home/data/lj/Piano/KEY_PRESS/checkpoint/experment' _C.INPUT_SIZE = { 'white':{ 'paperdata':[112,32], 'owndata':[112,32] }, 'black':{ 'paperdata':[112,32], 'owndata':[112,32] } } _C.ALPHA = { 'white':{ 'paperdata':3.0, 'owndata':3.0 }, 'black':{ 'paperdata':2.0, 'owndata':2.0 } }