(I am a beginner) I trained the model with yolov3-tiny.cfg and darknet53.con.74 because I had trouble loading the yolov3-tiny.weights(not sure if this matters). The model trained in colab for 3000 iterations (a couple hours) before It stopped. When I use these weights, the model performs poorly (I know tiny yolo is less precise but this this is extremely inaccurate) I'm pretty sure this is too few iterations, but when I load in the last training weights that are saved on my drive to continue training, I get this:
!./darknet detector train data/obj.data cfg/yolov3-tiny_training.cfg /mydrive/yolov3/yolov3-tiny_training_last.weights -dont_show
When I run this, I get this:
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
Does anyone know how to load the last weights in so it continues training?
