Eduardo Ottone
51bba0f5e8
- Removed restrictive filtering - now detects 79/80 classes by default - Added granular control: exclude_person, exclude_animals, exclude_vehicles - Added min_confidence parameter for quality control - Created COCO_CLASSES.md reference with all 80 detectable classes - Better logging showing included/excluded classes and counts - Can detect: furniture, electronics, food, accessories, sports equipment, etc. - Default: Only excludes person (79 classes available) - Much more flexible for diverse object detection scenarios - Perfect for extracting multiple objects from rich scenes
162 lines
3.4 KiB
Markdown
162 lines
3.4 KiB
Markdown
# COCO Dataset Classes Reference
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The YOLOv8 Object Detection Node can detect **80 different object classes** from the COCO dataset. Here's the complete list:
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## 🧑🤝🧑 People
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- **0**: person
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## 🚗 Vehicles
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- **1**: bicycle
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- **2**: car
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- **3**: motorcycle
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- **4**: airplane
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- **5**: bus
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- **6**: train
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- **7**: truck
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- **8**: boat
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## 🚦 Traffic & Outdoor
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- **9**: traffic light
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- **10**: fire hydrant
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- **11**: stop sign
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- **12**: parking meter
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- **13**: bench
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## 🐾 Animals
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- **14**: bird
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- **15**: cat
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- **16**: dog
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- **17**: horse
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- **18**: sheep
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- **19**: cow
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- **20**: elephant
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- **21**: bear
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- **22**: zebra
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- **23**: giraffe
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## 🎒 Accessories & Bags
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- **24**: backpack
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- **25**: umbrella
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- **26**: handbag
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- **27**: tie
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- **28**: suitcase
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## ⚽ Sports & Recreation
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- **29**: frisbee
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- **30**: skis
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- **31**: snowboard
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- **32**: sports ball
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- **33**: kite
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- **34**: baseball bat
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- **35**: baseball glove
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- **36**: skateboard
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- **37**: surfboard
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- **38**: tennis racket
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## 🍽️ Kitchen & Dining
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- **39**: bottle
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- **40**: wine glass
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- **41**: cup
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- **42**: fork
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- **43**: knife
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- **44**: spoon
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- **45**: bowl
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## 🍎 Food
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- **46**: banana
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- **47**: apple
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- **48**: sandwich
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- **49**: orange
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- **50**: broccoli
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- **51**: carrot
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- **52**: hot dog
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- **53**: pizza
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- **54**: donut
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- **55**: cake
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## 🏠 Furniture
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- **56**: chair
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- **57**: couch
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- **58**: potted plant
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- **59**: bed
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- **60**: dining table
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- **61**: toilet
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## 💻 Electronics
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- **62**: tv
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- **63**: laptop
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- **64**: mouse
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- **65**: remote
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- **66**: keyboard
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- **67**: cell phone
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## 🔌 Appliances
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- **68**: microwave
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- **69**: oven
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- **70**: toaster
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- **71**: sink
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- **72**: refrigerator
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## 📚 Personal Items
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- **73**: book
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- **74**: clock
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- **75**: vase
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- **76**: scissors
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- **77**: teddy bear
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- **78**: hair drier
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- **79**: toothbrush
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---
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## 🎛️ Filter Controls
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### Current Default Settings:
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- ✅ **All 80 classes enabled** by default
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- ❌ **exclude_person**: True (only excludes person)
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- ❌ **exclude_animals**: False (animals included)
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- ❌ **exclude_vehicles**: False (vehicles included)
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### Available Filters:
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- **exclude_person**: Skip person detection
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- **exclude_animals**: Skip all animals (classes 14-23)
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- **exclude_vehicles**: Skip all vehicles (classes 1-8)
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- **min_confidence**: Minimum confidence for any detection (0.1-0.9)
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### Examples:
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**Detect Everything (79 classes):**
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- exclude_person: True
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- exclude_animals: False
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- exclude_vehicles: False
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- **Result**: All objects except people
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**Objects Only (69 classes):**
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- exclude_person: True
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- exclude_animals: True
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- exclude_vehicles: False
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- **Result**: No people or animals, includes vehicles
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**Pure Objects (61 classes):**
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- exclude_person: True
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- exclude_animals: True
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- exclude_vehicles: True
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- **Result**: Only furniture, electronics, food, accessories
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**Everything (80 classes):**
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- exclude_person: False
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- exclude_animals: False
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- exclude_vehicles: False
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- **Result**: Detect absolutely everything including people
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---
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## 💡 Pro Tips:
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1. **Lower confidence** (0.1-0.3) = more detections, some false positives
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2. **Higher confidence** (0.5-0.8) = fewer but more accurate detections
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3. **Check console output** to see which classes are being excluded
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4. **Use padding** to get more context around detected objects
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5. **Adjust max_size** based on your output needs (128-1024px)
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Perfect for detecting multiple objects from a rich set of 80 different classes! 🎯
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