（我是一个初学者），我使用yolov3-tiny.cfg和darknet53.con.74训练了该模型，因为我无法加载yolov3-tiny.weights（不确定这是否重要）。在停止之前，该模型在colab中训练了3000次迭代（几个小时）。当我使用这些权重时，模型的性能会很差（我知道微小的yolo不够精确，但这是非常不准确的），我敢肯定这是很少的迭代，但是当我加载上次保存的训练权重时开车继续训练，我明白了：
!./darknet detector train data/obj.data cfg/yolov3-tiny_training.cfg /mydrive/yolov3/yolov3-tiny_training_last.weights -dont_show
当我运行它时，我得到了：
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
有人知道如何加载最后的砝码以便继续训练吗？