Epoch GPU_mem box_loss cls_loss dfl_loss

عنوان المحادثة: Epoch GPU_mem box_loss cls_loss dfl_loss Instances Siz...

التاريخ: 30.01.2026

التصنيف: 🖥️ أجهزة الكمبيوتر والمكونات المادية

إجمالي الرسائل: 2 | ياسر: 2 | M: 0

Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 1/50 0G 2.768 5.724 2.401 5 640: 100% ━━━━━━━━━━━━ 2/2 5.3s/it 10.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.0s/it 1.0s all 3 3 0 0 0 0 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 2/50 0G 2.887 6.312 2.713 2 640: 100% ━━━━━━━━━━━━ 2/2 3.2s/it 6.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.5it/s 0.7s all 3 3 0.00262 0.25 0.00486 0.000972 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 3/50 0G 2.975 5.28 2.489 6 640: 100% ━━━━━━━━━━━━ 2/2 3.1s/it 6.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.2it/s 0.8s all 3 3 0.00225 0.25 0.00329 0.000988 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 4/50 0G 2.712 5.333 2.4 5 640: 100% ━━━━━━━━━━━━ 2/2 3.2s/it 6.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.5it/s 0.7s all 3 3 0.00216 0.25 0.00237 0.000712 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 5/50 0G 2.951 6.515 2.647 2 640: 100% ━━━━━━━━━━━━ 2/2 2.9s/it 5.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.3it/s 0.8s all 3 3 0.00197 0.25 0.002 0.0004 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 6/50 0G 2.826 6.415 2.771 2 640: 100% ━━━━━━━━━━━━ 2/2 2.9s/it 5.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.3it/s 0.8s all 3 3 0.00191 0.25 0.00169 0.000169 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 7/50 0G 2.706 5.662 2.466 5 640: 100% ━━━━━━━━━━━━ 2/2 2.8s/it 5.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.6it/s 0.6s all 3 3 0.00191 0.25 0.00156 0.000156 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 8/50 0G 2.414 6.111 2.223 3 640: 100% ━━━━━━━━━━━━ 2/2 2.9s/it 5.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.5it/s 0.7s all 3 3 0 0 0 0 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 9/50 0G 2.28 5.433 2.014 3 640: 100% ━━━━━━━━━━━━ 2/2 2.9s/it 5.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.5it/s 0.7s all 3 3 0 0 0 0 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 10/50 0G 2.754 6.399 2.511 3 640: 100% ━━━━━━━━━━━━ 2/2 3.1s/it 6.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.4it/s 0.7s all 3 3 0 0 0 0

Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 11/50 0G 2.235 4.878 1.992 3 640: 100% ━━━━━━━━━━━━ 2/2 3.1s/it 6.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.4it/s 0.7s all 3 3 0 0 0 0 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 12/50 0G 2.074 5.018 1.905 5 640: 100% ━━━━━━━━━━━━ 2/2 2.9s/it 5.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.4it/s 0.7s all 3 3 0 0 0 0 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 13/50 0G 3.425 5.573 2.532 7 640: 100% ━━━━━━━━━━━━ 2/2 3.3s/it 6.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.4it/s 0.7s all 3 3 0 0 0 0EarlyStopping: Training stopped early as no improvement observed in last 10 epochs. Best results observed at epoch 3, best model saved as best.pt.To update EarlyStopping(patience=10) pass a new patience value, i.e. patience=300 or use patience=0 to disable EarlyStopping.13 epochs completed in 0.027 hours.Optimizer stripped from D:\my_files\programming\Projects\cam\runs\detect\supermarket_model\weights\last.pt, 6.2MBOptimizer stripped from D:\my_files\programming\Projects\cam\runs\detect\supermarket_model\weights\best.pt, 6.2MBValidating D:\my_files\programming\Projects\cam\runs\detect\supermarket_model\weights\best.pt...Ultralytics 8.4.9 Python-3.14.0 torch-2.10.0+cpu CPU (Intel Core i5-6500 3.20GHz)Model summary (fused): 73 layers, 3,007,403 parameters, 0 gradients, 8.1 GFLOPs Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.8it/s 0.6s all 3 3 0.00225 0.25 0.00329 0.000988 refrigerator 2 2 0.0045 0.5 0.00659 0.00198 shelf_rack 1 1 0 0 0 0Speed: 4.0ms preprocess, 142.9ms inference, 0.0ms loss, 19.0ms postprocess per imageResults saved to D:\my_files\programming\Projects\cam\runs\detect\supermarket_model==================================================Training Complete!==================================================Best model saved to: runs/detect/supermarket_model/weights/best.ptTo use your custom model in the surveillance app:1. Copy 'runs/detect/supermarket_model/weights/best.pt' to your project folder2. In main.py, change: YOLO('yolov8n.pt') to YOLO('best.pt')Ultralytics 8.4.9 Python-3.14.0 torch-2.10.0+cpu CPU (Intel Core i5-6500 3.20GHz)Model summary (fused): 73 layers, 3,007,403 parameters, 0 gradients, 8.1 GFLOPsval: Fast image access (ping: 0.10.0 ms, read: 1622.7458.8 MB/s, size: 433.4 KB)val: Scanning D:\my_files\programming\Projects\cam\dataset\val\labels.cache... 3 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 3/3 786.4Kit/s 0.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 1/1 1.9it/s 0.5s all 3 3 0.00225 0.25 0.00329 0.000988 refrigerator 2 2 0.0045 0.5 0.00659 0.00198 shelf_rack 1 1 0 0 0 0Speed: 4.0ms preprocess, 134.5ms inference, 0.0ms loss, 20.1ms postprocess per imageResults saved to D:\my_files\programming\Projects\cam\runs\detect\valValidation Results:mAP50: 0.003mAP50-95: 0.001Done!D:\my_files\programming\Projects\cam>