File size: 2,766 Bytes
bb51988 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
---
language:
- "en"
thumbnail: "https://example.com/path/to/your/thumbnail.jpg"
tags:
- yolo
- object-detection
- image-segmentation
- computer-vision
- human-body-parts
license: "mit"
datasets:
- custom_human_body_parts_dataset
metrics:
- mean_average_precision
- intersection_over_union
base_model: "ultralytics/yolov5yolov8x-seg"
---
# YOLO Segmentation Model for Human Body Parts and Objects
This model is a fine-tuned version of YOLOv5 for segmenting human body parts and objects. It can detect and segment 11 different classes including various body parts, outfits, and phones.
## Model Details
- **Model Type:** YOLOv8 for Instance Segmentation
- **Task:** Segmentation
- **Fine-tuning Dataset:** Custom dataset of human body parts and objects
- **Number of Classes:** 11
## Classes
The model can detect and segment the following classes:
0. Hair
1. Face
2. Neck
3. Arm
4. Hand
5. Back
6. Leg
7. Foot
8. Outfit
9. Person
10. Phone
## Usage
This model can be used for various applications, including:
- Human pose estimation
- Gesture recognition
- Fashion analysis
- Person tracking
- Human-computer interaction
For detailed usage instructions, please refer to the model's README file.
## Training Procedure
The model was fine-tuned on a custom dataset of annotated images containing human body parts and objects. The training process involved transfer learning from the base YOLOv8 model, with adjustments made to the final layers to accommodate the new class structure.
## Evaluation Results
(Note: Replace these placeholder metrics with your actual evaluation results)
lr/pg0:0.000572628
lr/pg1:0.000572628
lr/pg2:0.000572628
metrics/mAP50-95(B):0.53001
metrics/mAP50-95(M):0.42367
metrics/mAP50(B):0.69407
metrics/mAP50(M):0.61714
metrics/precision(B):0.7047
metrics/precision(M):0.68041
metrics/recall(B):0.68802
metrics/recall(M):0.62248
model/GFLOPs:344.557
model/parameters:71,761,441
model/speed_PyTorch(ms):5.813
train/box_loss:0.54718
train/cls_loss:0.52977
train/dfl_loss:0.95171
train/seg_loss:1.34628
val/box_loss:0.80538
val/cls_loss:0.83434
val/dfl_loss:1.18352
val/seg_loss:2.19488
## Limitations and Biases
- The model's performance may vary depending on lighting conditions and image quality.
- It may have difficulty with occluded or partially visible body parts.
- The model's performance on diverse body types and skin tones should be carefully evaluated to ensure fairness and inclusivity.
## Ethical Considerations
Users of this model should be aware of privacy concerns related to human body detection and ensure they have appropriate consent for its application. The model should not be used for surveillance or any application that could infringe on personal privacy without explicit consent. |