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Ultralytics YOLOv8.1.13 🚀 Python-3.8.18 torch-2.1.1+cu121 CUDA:0 (NVIDIA A100 80GB PCIe, 81050MiB)
Model summary (fused): 218 layers, 25840339 parameters, 0 gradients, 78.7 GFLOPs
[34m[1mval: [0mScanning /scratch/gilbreth/jpfinley/ultralytics/datasets/micro/valid/labels.cache... 15 images, 0 backgrounds, 0 corrupt: 100%|██████████| 15/15 [00:00<?, ?it/s][34m[1mval: [0mScanning /scratch/gilbreth/jpfinley/ultralytics/datasets/micro/valid/labels.cache... 15 images, 0 backgrounds, 0 corrupt: 100%|██████████| 15/15 [00:00<?, ?it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 0%| | 0/1 [00:00<?, ?it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:03<00:00, 3.53s/it] Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:03<00:00, 3.53s/it] all 15 218 0.657 0.665 0.648 0.248
Speed: 0.5ms preprocess, 5.5ms inference, 0.0ms loss, 24.3ms postprocess per image
Results saved to [1mruns/detect/val11[0m
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([0])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x2ba197827f10>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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fitness: 0.2883143632629532
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.24832])
names: {0: 'microglia'}
plot: True
results_dict: {'metrics/precision(B)': 0.6573764482274, 'metrics/recall(B)': 0.6651376146788991, 'metrics/mAP50(B)': 0.6482527443794568, 'metrics/mAP50-95(B)': 0.24832120980556388, 'fitness': 0.2883143632629532}
save_dir: PosixPath('runs/detect/val11')
speed: {'preprocess': 0.4602273305257161, 'inference': 5.4664770762125645, 'loss': 0.0004450480143229167, 'postprocess': 24.328311284383137}
task: 'detect'