Python YOLO darkent_images.py无法检测到对象
我训练了一个YOLOv3模型。当我尝试使用darknet可执行文件在测试图像上测试我的模型时，我可以得到检测到的对象。我使用以下命令：Python YOLO darkent_images.py无法检测到对象,python,object-detection,yolo,inference,darknet,Python,Object Detection,Yolo,Inference,Darknet,我训练了一个YOLOv3模型。当我尝试使用darknet可执行文件在测试图像上测试我的模型时，我可以得到检测到的对象。我使用以下命令： ./darknet detector test /home/goktug/projects/darknet/training/model/model_kitti.data /home/goktug/projects/darknet/training/model/yolov3-tiny_model_kitti.cfg /home/goktug/projects/d
./darknet detector test /home/goktug/projects/darknet/training/model/model_kitti.data /home/goktug/projects/darknet/training/model/yolov3-tiny_model_kitti.cfg /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights /home/goktug/projects/darknet/training/test_data/000001.png
python3 darknet_images.py --input /home/goktug/projects/darknet/training/test_data/000001.png --batch_size 1 --weights /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights --dont_show --ext_output --save_labels --config_file /home/goktug/projects/darknet/training/model/yolov3-tiny_model_kitti.cfg --data_file /home/goktug/projects/darknet/training/model/model_kitti.data
CUDA-version: 11000 (11000), cuDNN: 8.0.4, GPU count: 1
OpenCV version: 3.2.0
0 : compute_capability = 750, cudnn_half = 0, GPU: GeForce GTX 1650 with Max-Q Design
net.optimized_memory = 0
mini_batch = 1, batch = 64, time_steps = 1, train = 0
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 42 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 42 0.007 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_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 42 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 42 0.015 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.460
avg_outputs = 326536
Allocate additional workspace_size = 52.43 MB
Loading weights from /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights...
seen 64, trained: 2432 K-images (38 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Detection layer: 16 - type = 28
Detection layer: 23 - type = 28
/home/goktug/projects/darknet/training/test_data/000001.png: Predicted in 278.641000 milli-seconds.
Car: 90%
Car: 97%
Car: 98%
Try to load cfg: /home/goktug/projects/darknet/training/model/yolov3-tiny_model_kitti.cfg, weights: /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights, clear = 0
0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce GTX 1650 with Max-Q Design
net.optimized_memory = 0
mini_batch = 2, batch = 128, time_steps = 1, train = 0
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 42 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 42 0.007 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_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 42 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 42 0.015 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.460
avg_outputs = 326536
Allocate additional workspace_size = 52.43 MB
Try to load weights: /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights
Loading weights from /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights...
seen 64, trained: 2432 K-images (38 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Loaded - names_list: /home/goktug/projects/darknet/training/model/model_ktti.names, classes = 9
try to allocate additional workspace_size = 52.43 MB
CUDA allocate done!
Objects:
FPS: 2
./darknet detector test /home/goktug/projects/darknet/training/model/model_kitti.data /home/goktug/projects/darknet/training/model/yolov3-tiny_model_kitti.cfg /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights /home/goktug/projects/darknet/training/test_data/000001.png
python3 darknet_images.py --input /home/goktug/projects/darknet/training/test_data/000001.png --batch_size 1 --weights /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights --dont_show --ext_output --save_labels --config_file /home/goktug/projects/darknet/training/model/yolov3-tiny_model_kitti.cfg --data_file /home/goktug/projects/darknet/training/model/model_kitti.data
CUDA-version: 11000 (11000), cuDNN: 8.0.4, GPU count: 1
OpenCV version: 3.2.0
0 : compute_capability = 750, cudnn_half = 0, GPU: GeForce GTX 1650 with Max-Q Design
net.optimized_memory = 0
mini_batch = 1, batch = 64, time_steps = 1, train = 0
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 42 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 42 0.007 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_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 42 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 42 0.015 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.460
avg_outputs = 326536
Allocate additional workspace_size = 52.43 MB
Loading weights from /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights...
seen 64, trained: 2432 K-images (38 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Detection layer: 16 - type = 28
Detection layer: 23 - type = 28
/home/goktug/projects/darknet/training/test_data/000001.png: Predicted in 278.641000 milli-seconds.
Car: 90%
Car: 97%
Car: 98%
Try to load cfg: /home/goktug/projects/darknet/training/model/yolov3-tiny_model_kitti.cfg, weights: /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights, clear = 0
0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce GTX 1650 with Max-Q Design
net.optimized_memory = 0
mini_batch = 2, batch = 128, time_steps = 1, train = 0
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 42 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 42 0.007 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_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 42 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 42 0.015 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.460
avg_outputs = 326536
Allocate additional workspace_size = 52.43 MB
Try to load weights: /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights
Loading weights from /home/goktug/projects/darknet/training/trained_weights_1/yolov3-tiny_model_kitti_19000.weights...
seen 64, trained: 2432 K-images (38 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Loaded - names_list: /home/goktug/projects/darknet/training/model/model_ktti.names, classes = 9
try to allocate additional workspace_size = 52.43 MB
CUDA allocate done!
Objects:
FPS: 2