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Validating final-training-x/train3/weights/best.pt...
Ultralytics 8.3.82 🚀 Python-3.10.12 torch-2.5.1+cu124 CUDA:1 (NVIDIA GeForce RTX 4090, 24118MiB)
YOLO11x summary (fused): 190 layers, 56,837,419 parameters, 0 gradients, 194.5 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 51/51 [00:36
all 2215 26646 0.93 0.873 0.926 0.624
Person 2215 2468 0.982 0.99 0.993 0.915
Torso 2194 2441 0.943 0.94 0.972 0.695
Hand 2004 3739 0.929 0.869 0.925 0.619
Foot 1034 1950 0.874 0.773 0.839 0.45
Upper Leg 1593 3215 0.888 0.777 0.869 0.502
Lower Leg 1292 2541 0.909 0.812 0.896 0.536
Upper …
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Coco 2014 densepose relabeling with body parts. This dataset is formatted for ultralytics yolo. It's ready to go.
It contains the following classes:
'''
{
1: "Person",
2: "Torso",
3: "Hand",
4: "Foot",
5: "Upper Leg",
6: "Lower Leg",
7: "Upper Arm",
8: "Lower Arm",
9: "Head"
}
'''
It has the following format:
dataset/
├── images/
│ ├── train/ # Training images (e.g., .jpg or .png)
│ └── val/ # Validation images
├── labels/
│ ├── train/ # Training labels (one .txt per image)
│ └── val/ # Validation labels
└── data.yaml # YAML config file
You can train the model using something like:
'''from ultralytics import YOLO
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
model = YOLO("yolo11l.pt")
results = model.train(data="dataset/data.yaml", epochs=50, imgsz=640, device="0", batch=16, save=True)
''' -
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