diff --git a/filter/RunningLogs.txt b/filter/RunningLogs.txt
index 65b0d04..8d76c33 100644
--- a/filter/RunningLogs.txt
+++ b/filter/RunningLogs.txt
@@ -27,4 +27,10 @@ Note
 2350: V3.13 # V3.12, 换用普通交叉熵损失
 0012: V3.11 # 换用普通交叉熵损失
 0039: V3.11 # 级联分类，但使用两个独立模型
-0122: V3.15 # 删除author_info通道
\ No newline at end of file
+0122: V3.15 # 删除author_info通道
+0138: V3.15 # 最好的模型，使用自定义loss
+0038: V6.0  # CNN模型
+0128: V6.1  # Transformer模型（交叉熵损失）
+0219: V6.1  # MPS训练
+0242: V6.1  # 自定义loss
+0259: V6.1  # 调整学习率
\ No newline at end of file
diff --git a/filter/train.py b/filter/train.py
index 3952965..e206455 100644
--- a/filter/train.py
+++ b/filter/train.py
@@ -5,6 +5,7 @@ from torch.utils.data import DataLoader
 import torch.optim as optim
 from dataset import MultiChannelDataset
 from filter.modelV6_1 import VideoClassifierV6_1
+from filter.modelV3_15 import AdaptiveRecallLoss
 from sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score, classification_report
 import os
 import torch
@@ -58,7 +59,7 @@ class_weights = torch.tensor(
 )
 
 model = VideoClassifierV6_1().to(device)
-checkpoint_name = './filter/checkpoints/best_model_V6.2-mps.pt'
+checkpoint_name = './filter/checkpoints/best_model_V6.2-mps-adloss.pt'
 
 # 初始化tokenizer和embedding模型
 tokenizer = AutoTokenizer.from_pretrained("alikia2x/jina-embedding-v3-m2v-1024")
@@ -72,9 +73,9 @@ eval_interval = 20
 num_epochs = 20
 total_steps = samples_count * num_epochs / train_loader.batch_size
 warmup_rate = 0.1
-optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-5)
+optimizer = optim.AdamW(model.parameters(), lr=6e-5, weight_decay=1e-5)
 cosine_annealing_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps - int(total_steps * warmup_rate))
-warmup_scheduler = optim.lr_scheduler.LinearLR(optimizer, start_factor=0.1, end_factor=1.0, total_iters=int(total_steps * warmup_rate))
+warmup_scheduler = optim.lr_scheduler.LinearLR(optimizer, start_factor=0.14, end_factor=1.0, total_iters=int(total_steps * warmup_rate))
 scheduler = optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup_scheduler, cosine_annealing_scheduler], milestones=[int(total_steps * warmup_rate)])
 criterion = nn.CrossEntropyLoss(weight=class_weights).to(device)