在Google Colab上训练Yolov3，但它在4000次迭代后停止了.我该如何继续训练

Question

(我是初学者(我用yolov3-tiny.cfg和darknet53.con.74训练了这个模型，因为我在加载yolov3-miny.weights时遇到了问题(不确定这是否重要(。该模型在colab中训练了3000次迭代(几个小时(后才停止。当我使用这些权重时，模型表现不佳(我知道微小的yolo不太精确，但这非常不准确(。我很确定这是太少的迭代，但当我加载保存在驱动器上的最后一个训练权重以继续训练时，我会得到：

!./darknet detector train data/obj.data cfg/yolov3-tiny_training.cfg /mydrive/yolov3/yolov3-tiny_training_last.weights -dont_show

当我运行这个时，我得到这个：

CUDA-version: 10010 (10010), cuDNN: 7.6.5, GPU count: 1  
OpenCV version: 3.2.0
yolov3-tiny_training
0 : compute_capability = 370, cudnn_half = 0, GPU: Tesla K80 
net.optimized_memory = 0 
mini_batch = 4, batch = 64, time_steps = 1, train = 1 
layer   filters  size/strd(dil)      input                output
0 conv     16       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  16 0.150 BF
1 max                2x 2/ 2    416 x 416 x  16 ->  208 x 208 x  16 0.003 BF
2 conv     32       3 x 3/ 1    208 x 208 x  16 ->  208 x 208 x  32 0.399 BF
3 max                2x 2/ 2    208 x 208 x  32 ->  104 x 104 x  32 0.001 BF
4 conv     64       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  64 0.399 BF
5 max                2x 2/ 2    104 x 104 x  64 ->   52 x  52 x  64 0.001 BF
6 conv    128       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x 128 0.399 BF
7 max                2x 2/ 2     52 x  52 x 128 ->   26 x  26 x 128 0.000 BF
8 conv    256       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 256 0.399 BF
9 max                2x 2/ 2     26 x  26 x 256 ->   13 x  13 x 256 0.000 BF
10 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
11 max                2x 2/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.000 BF
12 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
13 conv    256       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 256 0.089 BF
14 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
15 conv     21       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x  21 0.004 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
17 route  13                                 ->   13 x  13 x 256 
18 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF
19 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128
20 route  19 8                               ->   26 x  26 x 384 
21 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF
22 conv     21       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x  21 0.007 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.449 
avg_outputs = 325057 
Allocate additional workspace_size = 12.46 MB 
Loading weights from /mydrive/yolov3/yolov3-tiny_training_last.weights...
seen 64, trained: 256 K-images (4 Kilo-batches_64) 
Done! Loaded 24 layers from weights-file 
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
Detection layer: 16 - type = 27 
Detection layer: 23 - type = 27 
Saving weights to /mydrive/yolov3/yolov3-tiny_training_final.weights
Create 6 permanent cpu-threads 

有人知道如何把最后的重量加进去，让它继续训练吗？