adding yolo to pipeline

.gitignore

@@ -1,3 +1,5 @@
 .venv/
 __pycache__/
-data/*.mov
+data/*
+segments/*
+segments/yolo/*

README.md

@@ -99,9 +99,10 @@ graph TD
 ## Repository Structure
 
 ```plaintext
-├── config.py               # 🔧 Central configuration and constants
+├── config.py               # 🔧 Central configuration, directories, and constants
 ├── video.py                # 🎬 Video classification and NFL play analysis
 ├── audio.py                # 🎙️ Audio transcription with NFL enhancements
+├── yolo_processor.py       # 🎯 YOLO object detection preprocessing
 ├── inference.py            # 🔄 Backward compatibility interface
 ├── run_all_clips.py        # 🚀 Main processing pipeline orchestrator
 ├── speed_test.py           # ⚡ Performance benchmarking tools
@@ -142,7 +143,7 @@ cd hf-video-scoring
 conda create -n nfl-play python=3.11 -y
 conda activate nfl-play
 conda install -y -c pytorch -c conda-forge pytorch torchvision torchaudio cpuonly
-pip install pytorchvideo huggingface_hub ffmpeg-python transformers openai-whisper
+pip install pytorchvideo huggingface_hub ffmpeg-python transformers openai-whisper ultralytics
 ```
 
 **Using pip in a virtualenv:**
@@ -197,7 +198,7 @@ This will:
 
 The pipeline is now optimized for continuous processing with separate video and audio phases:
 
-**🚀 Video-only processing (85% faster for real-time analysis):**
+**🚀 Default: YOLO + video analysis (auto-enabled for segments):**
 ```bash
 python run_all_clips.py --video-only
 ```
@@ -207,7 +208,7 @@ python run_all_clips.py --video-only
 python run_all_clips.py --audio-only
 ```
 
-**🎬 Full pipeline (video + audio in sequence):**
+**🎬 Full pipeline (YOLO + video + audio):**
 ```bash
 python run_all_clips.py
 ```
@@ -217,6 +218,11 @@ python run_all_clips.py
 python run_all_clips.py --video-only --max-clips 5
 ```
 
+**⚡ Skip YOLO for faster processing:**
+```bash
+python run_all_clips.py --no-yolo --video-only
+```
+
 The system:
 * Processes video classification and play analysis first (Phase 1)
 * Then processes audio transcription in batch (Phase 2, if enabled)
@@ -234,15 +240,15 @@ The system:
 # 1. Capture live screen content
 swift ContinuousScreenSplitter.swift
 
-# 2. Process video quickly for immediate play detection (in another terminal)
+# 2. Process with automatic YOLO object detection (default behavior)
 source .venv/bin/activate
-python run_all_clips.py --input-dir segments --video-only
+python run_all_clips.py --video-only
 
 # 3. View immediate NFL play analysis
 cat play_analysis.json | jq '.summary'
 
 # 4. Add transcripts later when time allows
-python run_all_clips.py --input-dir segments --audio-only
+python run_all_clips.py --audio-only
 
 # 5. View complete analysis
 cat transcripts.json | jq '.[] | select(. != "")'
@@ -292,6 +298,28 @@ This workflow captures live NFL content, automatically segments it, and then ana
 }
 ```
 
+## YOLO Object Detection Integration
+
+The system now includes **YOLOv11** integration for enhanced video analysis with object detection:
+
+### 🎯 **YOLO Enhancement Features**
+* **Player Detection**: Identifies football players, referees, and coaches
+* **Ball Tracking**: Detects footballs and other sports equipment  
+* **Spatial Analysis**: Provides bounding boxes for key game elements
+* **Visual Annotations**: Adds detection overlays while preserving original audio
+* **Selective Processing**: Only applied to video classification, audio uses original clips
+
+### 🔧 **YOLO Integration Workflow**
+1. **Phase 0**: Raw clips in `/segments/` → YOLO processing → `/segments/yolo/`
+2. **Phase 1**: Video classification uses YOLO-annotated clips
+3. **Phase 2**: Audio transcription uses original clips (preserves quality)
+
+### ⚡ **Performance Considerations**
+* **YOLO Model**: Nano size for speed vs. accuracy balance
+* **Parallel Processing**: Video and audio pipelines remain independent
+* **Auto-Enabled**: Automatically enabled for `segments` directory
+* **Control Flags**: Use `--no-yolo` to disable or `--use-yolo` to force enable
+
 ## Audio Transcription Features
 
 The system uses **Whisper-Medium** with NFL-specific enhancements for superior audio transcription:
@@ -344,14 +372,29 @@ After:  "is the sixth best quarterback in the NFL"
 
 ### Modular Architecture Benefits
 
-* **🔧 Easy Configuration**: All settings centralized in `config.py`
+* **🔧 Centralized Configuration**: All directories, paths, and settings in `config.py`
+* **📁 Flexible Directory Structure**: Configurable input/output/cache directories
 * **🎯 Focused Development**: Separate modules for video, audio, and configuration
 * **🧪 Better Testing**: Individual modules can be tested in isolation
 * **⚡ Performance Tuning**: Optimize video and audio processing independently
 * **📈 Scalability**: Add new models or sports without affecting existing code
 * **🔄 Backward Compatibility**: Existing scripts continue to work unchanged
 
-See `ARCHITECTURE.md` for detailed technical documentation.
+### Configurable Directory Structure
+
+All directories are now configurable through `config.py`:
+
+```python
+# Input directories
+DEFAULT_DATA_DIR = "data"                    # Default video clips
+DEFAULT_SEGMENTS_DIR = "segments"            # Screen capture segments  
+DEFAULT_YOLO_OUTPUT_DIR = "segments/yolo"    # YOLO processed clips
+
+# Cache directories (None = use system defaults)
+TORCH_HUB_CACHE_DIR = None                   # PyTorch model cache
+HUGGINGFACE_CACHE_DIR = None                 # HuggingFace model cache
+DEFAULT_TEMP_DIR = None                      # Temporary processing
+```
 
 ## Troubleshooting
 
@@ -385,18 +428,22 @@ See `ARCHITECTURE.md` for detailed technical documentation.
 ## Command Reference
 
 ```bash
-# Basic usage
-python run_all_clips.py                                    # Full pipeline
-python run_all_clips.py --video-only                       # Fast video analysis
+# Basic usage (auto-enables YOLO for segments directory)
+python run_all_clips.py                                    # Full pipeline with YOLO
+python run_all_clips.py --video-only                       # YOLO + video analysis only
 python run_all_clips.py --audio-only                       # Add transcripts later
 
+# YOLO control
+python run_all_clips.py --no-yolo --video-only             # Skip YOLO for speed
+python run_all_clips.py --use-yolo --input-dir data        # Force YOLO for other directories
+
 # Testing and development  
 python run_all_clips.py --max-clips 5                      # Limit to 5 clips
 python run_all_clips.py --video-only --max-clips 3        # Fast test with 3 clips
 python speed_test.py                                       # Performance benchmarking
 
 # Custom files and directories
-python run_all_clips.py --input-dir segments               # Different input directory
+python run_all_clips.py --input-dir data --no-yolo         # Process data directory without YOLO
 python run_all_clips.py --classification-file my_results.json  # Custom output file
 
 # Single clip analysis

classification.json

@@ -1,1674 +1,1674 @@
 {
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+  "segment_001_yolo.mov": [
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       "\"catching or throwing baseball\"",
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+      0.003380856942385435
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+      0.003095965599641204
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     [
-      "applauding",
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+      "\"playing ice hockey\"",
+      0.0027383891865611076
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     [
-      "\"catching or throwing softball\"",
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+      "\"ice skating\"",
+      0.0026859112549573183
     ],
     [
-      "\"passing American football (in game)\"",
-      0.0025873929262161255
+      "\"catching or throwing softball\"",
+      0.0026317567098885775
     ]
   ],
-  "segment_002.mov": [
+  "segment_002_yolo.mov": [
     [
       "\"mowing lawn\"",
-      0.004539191722869873
+      0.003576691960915923
     ],
     [
-      "\"riding or walking with horse\"",
-      0.002820482011884451
+      "\"golf driving\"",
+      0.002836523810401559
     ],
     [
-      "\"driving tractor\"",
-      0.002591945929452777
+      "archery",
+      0.0026338244788348675
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     [
-      "\"catching or throwing baseball\"",
-      0.002587514230981469
+      "\"driving tractor\"",
+      0.002595097990706563
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-      "archery",
-      0.0025512496940791607
+      "\"catching or throwing baseball\"",
+      0.0025854785926640034
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-  "segment_003.mov": [
+  "segment_003_yolo.mov": [
     [
       "\"hurling (sport)\"",
-      0.0035289316438138485
+      0.0039045403245836496
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     [
       "headbutting",
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+      0.002825796138495207
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     [
-      "\"passing American football (not in game)\"",
-      0.002668950939550996
+      "\"playing cricket\"",
+      0.0027307451236993074
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     [
-      "\"pumping fist\"",
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+      "\"passing American football (not in game)\"",
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-      "\"playing cricket\"",
-      0.002635735785588622
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+      "\"shot put\"",
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   ],
-  "segment_006.mov": [
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-      "\"riding mountain bike\"",
-      0.0030539515428245068
+      "\"spray painting\"",
+      0.002744637429714203
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-      "\"bench pressing\"",
-      0.0030068582855165005
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+      "\"making pizza\"",
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+      "barbequing",
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-  "segment_007.mov": [
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-      "\"changing oil\"",
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+      "\"pushing wheelchair\"",
+      0.0030422406271100044
     ],
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-      "\"changing oil\"",
-      0.002655936172232032
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+      0.002759539056569338
     ],
     [
-      "\"passing American football (in game)\"",
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     ]
   ],
-  "segment_073.mov": [
+  "segment_073_yolo.mov": [
     [
-      "\"passing American football (in game)\"",
-      0.006470989435911179
+      "\"passing American football (not in game)\"",
+      0.003243114333599806
     ],
     [
-      "\"passing American football (not in game)\"",
-      0.0025515288580209017
+      "\"catching or throwing softball\"",
+      0.002755112247541547
     ],
     [
-      "\"side kick\"",
-      0.0025365715846419334
+      "\"hitting baseball\"",
+      0.0027126993518322706
     ],
     [
-      "\"kicking field goal\"",
-      0.002492384286597371
+      "\"catching or throwing baseball\"",
+      0.002663438906893134
     ],
     [
-      "\"high kick\"",
-      0.002490945626050234
+      "dodgeball",
+      0.002640771446749568
     ]
   ],
-  "segment_074.mov": [
+  "segment_074_yolo.mov": [
     [
-      "\"passing American football (in game)\"",
-      0.004092047456651926
+      "\"catching or throwing softball\"",
+      0.003232127521187067
     ],
     [
-      "\"passing American football (not in game)\"",
-      0.0037548826076090336
+      "\"catching or throwing baseball\"",
+      0.002780261682346463
     ],
     [
-      "\"side kick\"",
-      0.0026034193579107523
+      "\"kicking field goal\"",
+      0.00266905571334064
     ],
     [
-      "\"kicking field goal\"",
-      0.0025540809147059917
+      "\"passing American football (not in game)\"",
+      0.0026120333932340145
     ],
     [
-      "\"catching or throwing baseball\"",
-      0.0025116554461419582
+      "\"hitting baseball\"",
+      0.0025939152110368013
     ]
   ],
-  "segment_075.mov": [
+  "segment_075_yolo.mov": [
     [
-      "\"passing American football (in game)\"",
-      0.006258341949433088
+      "\"catching or throwing baseball\"",
+      0.004522757604718208
     ],
     [
       "\"passing American football (not in game)\"",
-      0.002621858147904277
+      0.0029215679969638586
     ],
     [
       "\"kicking field goal\"",
-      0.0025423644110560417
+      0.0026764352805912495
     ],
     [
-      "\"catching or throwing baseball\"",
-      0.0024970977101475
+      "\"catching or throwing softball\"",
+      0.0026693218387663364
     ],
     [
-      "\"pumping fist\"",
-      0.0024926913902163506
+      "\"hitting baseball\"",
+      0.0025645860005170107
     ]
   ],
-  "segment_076.mov": [
+  "segment_076_yolo.mov": [
     [
       "\"hurling (sport)\"",
-      0.003916610963642597
+      0.0032000483479350805
     ],
     [
       "headbutting",
-      0.0029018200002610683
+      0.0027553813997656107
     ],
     [
-      "\"passing American football (in game)\"",
-      0.0027735705953091383
+      "\"hitting baseball\"",
+      0.0027151587419211864
     ],
     [
-      "\"passing American football (not in game)\"",
-      0.00273358216509223
+      "\"playing cricket\"",
+      0.002658894518390298
     ],
     [
-      "\"kicking field goal\"",
-      0.0026465952396392822
+      "\"catching or throwing baseball\"",
+      0.0026289441157132387
     ]
   ],
   "segment_077.mov": []

config.py

@@ -19,6 +19,11 @@ DEVICE = torch.device("cpu")  # Use "cuda" for GPU acceleration if available
 AUDIO_MODEL_NAME = "openai/whisper-medium"  # Options: whisper-base, whisper-medium, whisper-large
 AUDIO_DEVICE = -1  # -1 for CPU, 0+ for GPU device index
 
+# YOLO Object Detection Model
+YOLO_MODEL_SIZE = "nano"  # Options: nano, small, medium, large, xlarge
+YOLO_CONFIDENCE_THRESHOLD = 0.25  # Detection confidence threshold
+YOLO_IMAGE_SIZE = 640  # Input image size for YOLO inference
+
 # ============================================================================
 # VIDEO PROCESSING PARAMETERS
 # ============================================================================
@@ -146,6 +151,32 @@ DEFAULT_CLASSIFICATION_FILE = "classification.json"
 DEFAULT_TRANSCRIPT_FILE = "transcripts.json"
 DEFAULT_PLAY_ANALYSIS_FILE = "play_analysis.json"
 
+# ============================================================================
+# DIRECTORY STRUCTURE AND PATHS
+# ============================================================================
+# 
+# All directory paths are centrally configured here. This provides:
+# - Consistent paths across all modules
+# - Easy customization for different deployment scenarios
+# - Clear separation between input, output, cache, and temporary directories
+# - Support for both relative and absolute paths
+#
+# To customize directories, modify the values below or set environment variables
+# before importing this module.
+
+# Input directories
+DEFAULT_DATA_DIR = "data"                    # Default video clips directory
+DEFAULT_SEGMENTS_DIR = "segments"            # Screen capture segments directory
+DEFAULT_YOLO_OUTPUT_DIR = "segments/yolo"    # YOLO processed clips output
+
+# Cache and temporary directories (None = use system defaults)
+DEFAULT_CACHE_DIR = None                     # General cache (~/.cache)
+DEFAULT_TEMP_DIR = None                      # Temporary files (/tmp)
+
+# Model storage paths (None = use default locations)
+TORCH_HUB_CACHE_DIR = None                   # PyTorch hub models (~/.cache/torch/hub)
+HUGGINGFACE_CACHE_DIR = None                 # HuggingFace models (~/.cache/huggingface)
+
 # Progress saving intervals
 VIDEO_SAVE_INTERVAL = 5   # Save video results every N clips
 AUDIO_SAVE_INTERVAL = 3   # Save audio results every N clips

inference.py

@@ -23,6 +23,7 @@ from typing import List, Tuple
 # Import from new modular structure
 from video import predict_clip, analyze_play_state, detect_play_boundaries
 from audio import transcribe_clip, load_audio, apply_sports_corrections, fuzzy_sports_corrections
+from config import DEFAULT_DATA_DIR
 
 # Re-export all functions for backward compatibility
 __all__ = [
@@ -45,7 +46,7 @@ def main():
     """
     if len(sys.argv) < 2:
         print("Usage: python inference.py <path_to_video_clip>")
-        print("Example: python inference.py data/segment_001.mov")
+        print(f"Example: python inference.py {DEFAULT_DATA_DIR}/segment_001.mov")
         sys.exit(1)
     
     clip_path = sys.argv[1]

play_analysis.json

@@ -1,2054 +1,2054 @@
 {
   "clips": [
     {
-      "filename": "segment_001.mov",
+      "filename": "segment_001_yolo.mov",
       "play_state": "play_action",
-      "confidence": 0.008741466095671058,
+      "confidence": 0.009108579251915216,
       "classifications": [
         [
           "\"catching or throwing baseball\"",
-          0.003259257646277547
+          0.003380856942385435
         ],
         [
           "\"playing cricket\"",
-          0.002848330419510603
+          0.003095965599641204
         ],
         [
-          "applauding",
-          0.0027428145986050367
+          "\"playing ice hockey\"",
+          0.0027383891865611076
         ],
         [
-          "\"catching or throwing softball\"",
-          0.002633878029882908
+          "\"ice skating\"",
+          0.0026859112549573183
         ],
         [
-          "\"passing American football (in game)\"",
-          0.0025873929262161255
+          "\"catching or throwing softball\"",
+          0.0026317567098885775
         ]
       ]
     },
     {
-      "filename": "segment_002.mov",
+      "filename": "segment_002_yolo.mov",
       "play_state": "play_action",
-      "confidence": 0.002587514230981469,
+      "confidence": 0.0025854785926640034,
       "classifications": [
         [
           "\"mowing lawn\"",
-          0.004539191722869873
+          0.003576691960915923
         ],
         [
-          "\"riding or walking with horse\"",
-          0.002820482011884451
+          "\"golf driving\"",
+          0.002836523810401559
         ],
         [
-          "\"driving tractor\"",
-          0.002591945929452777
+          "archery",
+          0.0026338244788348675
         ],
         [
-          "\"catching or throwing baseball\"",
-          0.002587514230981469
+          "\"driving tractor\"",
+          0.002595097990706563
         ],
         [
-          "archery",
-          0.0025512496940791607
+          "\"catching or throwing baseball\"",
+          0.0025854785926640034
         ]
       ]
     },
     {
-      "filename": "segment_003.mov",
+      "filename": "segment_003_yolo.mov",
       "play_state": "play_action",
-      "confidence": 0.002635735785588622,
+      "confidence": 0.0027307451236993074,
       "classifications": [
         [
           "\"hurling (sport)\"",
-          0.0035289316438138485
+          0.0039045403245836496
         ],
         [
           "headbutting",
-          0.0029045743867754936
+          0.002825796138495207
         ],
         [
-          "\"passing American football (not in game)\"",
-          0.002668950939550996
+          "\"playing cricket\"",
+          0.0027307451236993074
         ],
         [
-          "\"pumping fist\"",
-          0.0026475831400603056
+          "\"passing American football (not in game)\"",
+          0.0026762450579553843
         ],
         [
-          "\"playing cricket\"",
-          0.002635735785588622
+          "applauding",
+          0.0026124196592718363
         ]
       ]
     },
     {
-      "filename": "segment_004.mov",
+      "filename": "segment_004_yolo.mov",
       "play_state": "non_play",
-      "confidence": 0.00304257869720459,
+      "confidence": 0.0026764876674860716,
       "classifications": [
         [
-          "applauding",
-          0.00304257869720459
+          "headbutting",
+          0.004119096323847771
         ],
         [
-          "headbutting",
-          0.0029725206550210714
+          "\"hurling (sport)\"",
+          0.0029537260998040438
         ],
         [
-          "\"pumping fist\"",
-          0.0029444803949445486
+          "applauding",
+          0.0026764876674860716
         ],
         [
-          "\"hurling (sport)\"",
-          0.0027822419069707394
+          "\"passing American football (not in game)\"",
+          0.002594469813629985
         ],
         [
-          "\"shaking hands\"",
-          0.002624393440783024
+          "\"playing ice hockey\"",
+          0.0025658044032752514
         ]
       ]
     },
     {
-      "filename": "segment_005.mov",
-      "play_state": "play_action",
-      "confidence": 0.003370030550286174,
+      "filename": "segment_005_yolo.mov",
+      "play_state": "unknown",
+      "confidence": 0.0,
       "classifications": [
         [
-          "\"catching or throwing baseball\"",
-          0.003370030550286174
+          "\"javelin throw\"",
+          0.0033433844801038504
         ],
         [
-          "\"pumping fist\"",
-          0.00289507070556283
+          "\"eating hotdog\"",
+          0.00290647498331964
         ],
         [
-          "\"catching or throwing frisbee\"",
-          0.0026615660171955824
+          "\"throwing discus\"",
+          0.0027681186329573393
         ],
         [
-          "\"eating hotdog\"",
-          0.002651733811944723
+          "\"catching or throwing frisbee\"",
+          0.002742217620834708
         ],
         [
-          "\"javelin throw\"",
-          0.0026478723157197237
+          "\"shot put\"",
+          0.00260338606312871
         ]
       ]
     },
     {
-      "filename": "segment_006.mov",
+      "filename": "segment_006_yolo.mov",
       "play_state": "unknown",
       "confidence": 0.0,
       "classifications": [
         [
-          "\"riding mountain bike\"",
-          0.0030539515428245068
+          "\"spray painting\"",
+          0.002744637429714203
         ],
         [
-          "\"bench pressing\"",
-          0.0030068582855165005
+          "\"checking tires\"",
+          0.002715102629736066
         ],
         [
-          "\"changing wheel\"",
-          0.0028557234909385443
+          "\"bench pressing\"",
+          0.002695797011256218
         ],
         [
-          "\"checking tires\"",
-          0.002828161232173443
+          "\"making pizza\"",
+          0.002596484264358878
         ],
         [
-          "\"changing oil\"",
-          0.0025825132615864277
+          "barbequing",
+          0.0025805369950830936
         ]
       ]
     },
     {
-      "filename": "segment_007.mov",
+      "filename": "segment_007_yolo.mov",
       "play_state": "unknown",
       "confidence": 0.0,
       "classifications": [
         [
-          "\"changing oil\"",
-          0.003507673041895032
+          "\"pushing wheelchair\"",
+          0.0030422406271100044
         ],
         [
-          "\"pushing wheelchair\"",
-          0.0028540242929011583
+          "\"mowing lawn\"",
+          0.0028666346333920956
         ],
         [
           "motorcycling",
-          0.002735486486926675
+          0.0027028322219848633
         ],
         [
-          "\"checking tires\"",
-          0.0026065800338983536
+          "\"changing oil\"",
+          0.0026753153651952744
         ],
         [
-          "\"changing wheel\"",
-          0.0026001674123108387
+          "\"riding mountain bike\"",
+          0.0026005778927356005
         ]
       ]
     },
     {
-      "filename": "segment_008.mov",
+      "filename": "segment_008_yolo.mov",
       "play_state": "unknown",
       "confidence": 0.0,
       "classifications": [
         [
-          "motorcycling",
-          0.0034892940893769264
+          "\"pushing wheelchair\"",
+          0.0028705329168587923
         ],
         [
-          "\"riding mountain bike\"",
-          0.0027264610398560762
+          "bobsledding",
+          0.002846728079020977
         ],
         [
-          "\"pushing wheelchair\"",
-          0.0026729153469204903
+          "headbutting",
+          0.0026647611521184444
         ],
         [
-          "\"changing oil\"",
-          0.002655936172232032
+          "\"driving car\"",
+          0.0026486360002309084
         ],
         [
-          "snowmobiling",
-          0.002622972708195448
+          "motorcycling",
+          0.002630403032526374
         ]
       ]
     },
     {
-      "filename": "segment_009.mov",
+      "filename": "segment_009_yolo.mov",
       "play_state": "play_active",
-      "confidence": 0.02098492393270135,
+      "confidence": 0.010849987156689167,
       "classifications": [
         [
-          "\"kicking field goal\"",
-          0.0051559642888605595
+          "bobsledding",
+          0.002958092140033841
         ],
         [
-          "\"tossing coin\"",
-          0.002858997555449605
+          "\"high kick\"",
+          0.002759539056569338
         ],
         [
-          "\"passing American football (in game)\"",
-          0.002840855158865452
+          "\"milking cow\"",
+          0.0027191024273633957
         ],
         [
-          "\"high kick\"",
-          0.0024956425186246634
+          "\"kicking field goal\"",
+          0.0026654545217752457
         ],
         [
           "headbutting",
-          0.002494481625035405
+          0.002656013937667012
         ]
       ]
     },
     {
-      "filename": "segment_010.mov",
+      "filename": "segment_010_yolo.mov",
       "play_state": "play_active",
-      "confidence": 0.011602595914155245,
+      "confidence": 0.010626845993101597,
       "classifications": [
         [
-          "\"tossing coin\"",
-          0.003494303673505783
+          "headbutting",
+          0.0029409260023385286
         ],
         [
-          "headbutting",
-          0.003139702370390296
+          "\"shaking hands\"",
+          0.0026740378234535456
         ],
         [
-          "\"kicking field goal\"",
-          0.003000226803123951
+          "\"high kick\"",
+          0.0026704464107751846
         ],
         [
-          "\"passing American football (in game)\"",
-          0.0028010711539536715
+          "\"kicking field goal\"",
+          0.002642976585775614
         ],
         [
-          "\"pumping fist\"",
-          0.0026133027859032154
+          "celebrating",
+          0.002638082252815366
         ]
       ]
     },
     {
-      "filename": "segment_011.mov",
+      "filename": "segment_011_yolo.mov",
       "play_state": "play_active",
-      "confidence": 0.01098605478182435,
+      "confidence": 0.008274616673588753,
       "classifications": [
         [
-          "headbutting",
-          0.0035944455303251743
+          "\"kicking field goal\"",
+          0.004137308336794376
         ],
         [
-          "\"kicking field goal\"",
-          0.002746515441685915
+          "applauding",
+          0.0026362661737948656
         ],
         [
-          "\"passing American football (in game)\"",
-          0.00274651194922626
+          "\"playing volleyball\"",
+          0.0026246444322168827
         ],
         [
-          "cheerleading",
-          0.0027097328566014767
+          "\"passing American football (not in game)\"",
+          0.002608613111078739
         ],
         [
-          "\"playing ice hockey\"",
-          0.002675419207662344
+          "headbutting",
+          0.0025905300863087177
         ]
       ]
     },
     {
-      "filename": "segment_012.mov",
+      "filename": "segment_012_yolo.mov",
       "play_state": "non_play",
-      "confidence": 0.0027217946480959654,
+      "confidence": 0.002713675843551755,
       "classifications": [
-        [
-          "cheerleading",
-          0.0033036908134818077
-        ],
         [
           "\"dancing macarena\"",
-          0.0031919179018586874
+          0.003159651532769203
         ],
         [
-          "applauding",
-          0.0027217946480959654
+          "cheerleading",
+          0.0030827471055090427
         ],
         [
           "\"hitting baseball\"",
-          0.0026708708610385656
+          0.00298778316937387
+        ],
+        [
+          "applauding",
+          0.002713675843551755
         ],
         [
           "\"garbage collecting\"",
-          0.0026213040109723806
+          0.0025780571158975363
         ]
       ]
     },
     {
-      "filename": "segment_013.mov",
+      "filename": "segment_013_yolo.mov",
       "play_state": "play_active",
-      "confidence": 0.005543170962482691,
+      "confidence": 0.005786340218037367,
       "classifications": [
+        [
+          "applauding",
+          0.0031382013112306595
+        ],
         [
           "\"pumping fist\"",
-          0.0035472228191792965
+          0.0029212948866188526
         ],
         [
           "\"kicking field goal\"",
-          0.0027715854812413454
+          0.0028931701090186834
         ],
         [
           "\"dunking basketball\"",
-          0.0027416404336690903
-        ],
-        [
-          "applauding",
-          0.002673310926184058
+          0.002754194661974907
         ],
         [
           "celebrating",
-          0.002629267517477274
+          0.0026313187554478645
         ]
       ]
     },
     {
-      "filename": "segment_014.mov",
-      "play_state": "unknown",
-      "confidence": 0.0,
+      "filename": "segment_014_yolo.mov",
+      "play_state": "play_active",
+      "confidence": 0.005094448570162058,
       "classifications": [
         [
           "headbutting",
-          0.0038350492250174284
+          0.003813667455688119
         ],
         [
-          "bobsledding",
-          0.002937863813713193
+          "\"punching person (boxing)\"",
+          0.003006032668054104
         ],
         [
-          "\"playing ice hockey\"",
-          0.002785387681797147
+          "\"punching bag\"",
+          0.0028552233707159758
         ],
         [
-          "\"punching person (boxing)\"",
-          0.002671806840226054
+          "\"pumping fist\"",
+          0.002737470669671893
         ],
         [
-          "\"changing wheel\"",
-          0.0026315213181078434
+          "\"high kick\"",
+          0.002547224285081029
         ]
       ]
     },
     {
-      "filename": "segment_015.mov",
+      "filename": "segment_015_yolo.mov",
       "play_state": "play_active",
-      "confidence": 0.020375477615743876,
+      "confidence": 0.013546076137572527,
       "classifications": [
         [
-          "\"passing American football (in game)\"",
-          0.00389948021620512
+          "\"kicking field goal\"",
+          0.0041602421551942825
         ],
         [
-          "\"side kick\"",
-          0.003301523393020034
+          "\"passing American football (not in game)\"",
+          0.0028576047625392675
         ],
         [
-          "\"kicking field goal\"",
-          0.002986735198646784
+          "\"juggling soccer ball\"",
+          0.002675252500921488
         ],
         [
-          "\"passing American football (not in game)\"",
-          0.0026217757258564234
+          "\"catching or throwing baseball\"",
+          0.0026170925702899694
         ],
         [
-          "\"catching or throwing baseball\"",
-          0.002540471963584423
+          "\"passing American football (in game)\"",
+          0.002612795913591981
         ]
       ]
     },
     {
-      "filename": "segment_016.mov",
+      "filename": "segment_016_yolo.mov",
       "play_state": "play_active",
-      "confidence": 0.02814871072769165,
+      "confidence": 0.01208669226616621,
       "classifications": [
         [
-          "\"passing American football (in game)\"",
-          0.006536518689244986
+          "\"passing American football (not in game)\"",
+          0.003784941276535392
         ],
         [
-          "\"side kick\"",
-          0.0025378491263836622
+          "\"kicking field goal\"",
+          0.003416945692151785
         ],
         [
-          "\"passing American football (not in game)\"",
-          0.0025076796300709248
+          "\"passing American football (in game)\"",
+          0.00262640044093132
         ],
         [
-          "\"kicking field goal\"",
-          0.002507618861272931
+          "\"catching or throwing baseball\"",
+          0.002601443789899349
         ],
         [
-          "\"high kick\"",
-          0.0024923686869442463
+          "\"juggling soccer ball\"",
+          0.0025872692931443453
         ]
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+      "clip_index": 54,
+      "clip_name": "segment_055_yolo.mov",
+      "confidence": 0.0026663222815841436
     },
     {
       "type": "play_start",
-      "clip_index": 59,
-      "clip_name": "segment_060.mov",
-      "confidence": 0.02252253331243992
+      "clip_index": 58,
+      "clip_name": "segment_059_yolo.mov",
+      "confidence": 0.0025341541040688753
     },
     {
       "type": "play_end",
       "clip_index": 64,
-      "clip_name": "segment_065.mov",
-      "confidence": 0.016939261462539434
+      "clip_name": "segment_065_yolo.mov",
+      "confidence": 0.010609124321490526
     },
     {
       "type": "play_start",
       "clip_index": 67,
-      "clip_name": "segment_068.mov",
-      "confidence": 0.01660818513482809
+      "clip_name": "segment_068_yolo.mov",
+      "confidence": 0.005456733051687479
     },
     {
       "type": "play_end",
       "clip_index": 75,
-      "clip_name": "segment_076.mov",
-      "confidence": 0.010840331669896841
+      "clip_name": "segment_076_yolo.mov",
+      "confidence": 0.005287838634103537
     }
   ],
   "summary": {
     "total_clips": 77,
-    "play_active_clips": 45,
-    "play_action_clips": 5,
-    "non_play_clips": 3,
-    "unknown_clips": 24,
-    "detected_play_starts": 11,
-    "detected_play_ends": 12
+    "play_active_clips": 30,
+    "play_action_clips": 18,
+    "non_play_clips": 4,
+    "unknown_clips": 25,
+    "detected_play_starts": 9,
+    "detected_play_ends": 10
   }
 }

requirements.txt

@@ -6,3 +6,5 @@ huggingface_hub
 ffmpeg-python
 # For high-quality NFL broadcast audio transcription
 openai-whisper
+# For YOLO object detection preprocessing
+ultralytics

run_all_clips.py

@@ -21,18 +21,21 @@ from typing import Dict, List, Any, Optional
 # Import from new modular structure
 from video import predict_clip, analyze_play_state, detect_play_boundaries
 from audio import transcribe_clip
+from yolo_processor import FootballDetector, preprocess_segments_with_yolo
 from config import (
     SUPPORTED_VIDEO_FORMATS, DEFAULT_CLASSIFICATION_FILE, DEFAULT_TRANSCRIPT_FILE,
-    DEFAULT_PLAY_ANALYSIS_FILE, VIDEO_SAVE_INTERVAL, AUDIO_SAVE_INTERVAL
+    DEFAULT_PLAY_ANALYSIS_FILE, VIDEO_SAVE_INTERVAL, AUDIO_SAVE_INTERVAL,
+    DEFAULT_DATA_DIR, DEFAULT_SEGMENTS_DIR, DEFAULT_YOLO_OUTPUT_DIR
 )
 
 
-def main(input_dir: str = "data", 
+def main(input_dir: str = DEFAULT_SEGMENTS_DIR, 
          classification_file: str = DEFAULT_CLASSIFICATION_FILE, 
          transcript_file: str = DEFAULT_TRANSCRIPT_FILE, 
          play_analysis_file: str = DEFAULT_PLAY_ANALYSIS_FILE, 
          skip_audio: bool = False, 
-         max_clips: Optional[int] = None) -> Dict[str, Any]:
+         max_clips: Optional[int] = None,
+         use_yolo: bool = None) -> Dict[str, Any]:
     """
     Main processing function for NFL play detection pipeline.
     
@@ -43,6 +46,7 @@ def main(input_dir: str = "data",
         play_analysis_file: Output file for play analysis results
         skip_audio: If True, skip audio transcription (Phase 2)
         max_clips: Limit processing to first N clips (for testing)
+        use_yolo: If True, preprocess clips with YOLO. If None, auto-enable for segments directory
         
     Returns:
         Dictionary with processing statistics and results
@@ -60,6 +64,46 @@ def main(input_dir: str = "data",
     if max_clips and max_clips > 0:
         clips = clips[:max_clips]
         print(f"🧪 TESTING MODE: Limited to first {len(clips)} clips")
+    
+    # Auto-enable YOLO for segments directory if not explicitly specified
+    if use_yolo is None:
+        use_yolo = (input_dir == DEFAULT_SEGMENTS_DIR or input_dir.endswith('segments'))
+        if use_yolo:
+            print(f"🎯 AUTO-ENABLING YOLO: Detected segments directory '{input_dir}'")
+            print(f"   Use --no-yolo to disable YOLO preprocessing")
+    
+    # PHASE 0: YOLO Preprocessing (if enabled)
+    yolo_clips_map = {}  # Map original clip -> YOLO processed clip
+    original_clips = clips.copy()  # Keep original clips for audio processing
+    
+    if use_yolo:
+        print(f"\n🎯 PHASE 0: YOLO OBJECT DETECTION PREPROCESSING")
+        print("=" * 70)
+        print("Preprocessing clips with YOLOv11 football detection...")
+        
+        # Process clips with YOLO if input_dir is segments
+        if input_dir == DEFAULT_SEGMENTS_DIR or input_dir.endswith('segments'):
+            football_detector = FootballDetector(
+                segments_dir=input_dir,
+                yolo_output_dir=DEFAULT_YOLO_OUTPUT_DIR
+            )
+            
+            processed_clips = football_detector.process_all_segments(max_clips=max_clips)
+            
+            # Create mapping from original clips to YOLO processed clips
+            for original_clip in clips:
+                yolo_clip = football_detector.get_processed_clip_path(original_clip)
+                if yolo_clip:
+                    yolo_clips_map[original_clip] = yolo_clip
+            
+            # Use YOLO processed clips for video classification
+            clips = [yolo_clips_map.get(clip, clip) for clip in clips]
+            
+            print(f"✓ YOLO preprocessing complete: {len(processed_clips)} clips processed")
+        else:
+            print(f"⚠️  YOLO preprocessing skipped: not processing segments directory")
+            print(f"   Current input directory: {input_dir}")
+            print(f"   YOLO preprocessing only works with '{DEFAULT_SEGMENTS_DIR}' directory")
 
     classification_results = {}
     transcript_results = {}
@@ -187,9 +231,12 @@ def main(input_dir: str = "data",
         print("=" * 60)
         print("Processing all audio files in batch...")
         
-        for i, clip in enumerate(clips, 1):
+        # Use original clips for audio transcription (not YOLO processed)
+        audio_clips = original_clips if use_yolo else clips
+        
+        for i, clip in enumerate(audio_clips, 1):
             clip_name = os.path.basename(clip)
-            print(f"\n[{i}/{len(clips)}] Transcribing audio: {clip_name}")
+            print(f"\n[{i}/{len(audio_clips)}] Transcribing audio: {clip_name}")
             
             try:
                 transcript = transcribe_clip(clip)
@@ -203,7 +250,7 @@ def main(input_dir: str = "data",
                 transcript_results[clip_name] = ""
 
             # Write incremental results after every N clips or at the end
-            if i % AUDIO_SAVE_INTERVAL == 0 or i == len(clips):
+            if i % AUDIO_SAVE_INTERVAL == 0 or i == len(audio_clips):
                 try:
                     with open(transcript_file, 'w') as f:
                         json.dump(transcript_results, f, indent=2)
@@ -214,8 +261,10 @@ def main(input_dir: str = "data",
         print(f"\n��� PHASE 2 COMPLETE - AUDIO TRANSCRIPTION")
         print("=" * 60)
         transcribed_clips = len([t for t in transcript_results.values() if t.strip()])
-        print(f"  Clips with transcripts: {transcribed_clips}/{len(clips)}")
+        print(f"  Clips with transcripts: {transcribed_clips}/{len(audio_clips)}")
         print(f"  ✓ Transcripts saved to {transcript_file}")
+        if use_yolo:
+            print(f"  ℹ️  Audio processed from original clips (not YOLO-annotated)")
     else:
         print(f"\n⏭️  PHASE 2 SKIPPED - Audio transcription disabled")
         print("=" * 60)
@@ -236,15 +285,18 @@ def main(input_dir: str = "data",
         "video_processed": len(clips),
         "audio_processed": len(transcript_results) if not skip_audio else 0,
         "play_boundaries": len(boundaries),
+        "yolo_processed": len(yolo_clips_map) if use_yolo else 0,
         "processing_time_saved": "~85% faster" if skip_audio else "full processing"
     }
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='NFL Play Detection Pipeline - Optimized for continuous processing')
-    parser.add_argument('--input-dir', default='data', help='Directory containing video clips (default: data)')
+    parser.add_argument('--input-dir', default=DEFAULT_SEGMENTS_DIR, help=f'Directory containing video clips (default: {DEFAULT_SEGMENTS_DIR})')
     parser.add_argument('--video-only', action='store_true', help='Process only video classification (85%% faster)')
     parser.add_argument('--audio-only', action='store_true', help='Process only audio transcription (requires existing classification.json)')
+    parser.add_argument('--use-yolo', action='store_true', help='Force enable YOLOv11 object detection preprocessing')
+    parser.add_argument('--no-yolo', action='store_true', help='Disable YOLOv11 preprocessing (overrides auto-enable for segments)')
     parser.add_argument('--max-clips', type=int, help='Limit processing to first N clips (for testing)')
     parser.add_argument('--classification-file', default=DEFAULT_CLASSIFICATION_FILE, help='Video classification output file')
     parser.add_argument('--transcript-file', default=DEFAULT_TRANSCRIPT_FILE, help='Audio transcript output file')
@@ -267,7 +319,22 @@ if __name__ == "__main__":
         try:
             with open(args.classification_file, 'r') as f:
                 classification_data = json.load(f)
-            clips = [os.path.join(args.input_dir, clip_name) for clip_name in classification_data.keys()]
+            
+            # For audio-only mode, always use original clips (not YOLO processed)
+            clips = []
+            for clip_name in classification_data.keys():
+                if clip_name.endswith('_yolo.mov') or clip_name.endswith('_yolo.mp4'):
+                    # Remove _yolo suffix to get original clip name
+                    original_name = clip_name.replace('_yolo.mov', '.mov').replace('_yolo.mp4', '.mp4')
+                    original_path = os.path.join(args.input_dir, original_name)
+                    if os.path.exists(original_path):
+                        clips.append(original_path)
+                    else:
+                        print(f"[WARN] Original clip not found for {clip_name}, using processed version")
+                        clips.append(os.path.join(args.input_dir, clip_name))
+                else:
+                    # Use clip as-is
+                    clips.append(os.path.join(args.input_dir, clip_name))
             
             # Limit clips for testing if specified
             if args.max_clips and args.max_clips > 0:
@@ -275,6 +342,8 @@ if __name__ == "__main__":
                 print(f"🧪 TESTING MODE: Limited to first {len(clips)} clips")
             else:
                 print(f"Found {len(clips)} clips from existing classification data")
+            
+            print(f"ℹ️  Audio processing will use original clips (not YOLO-processed)")
         except Exception as e:
             print(f"[ERROR] Failed to load classification file: {e}")
             exit(1)
@@ -314,6 +383,14 @@ if __name__ == "__main__":
         skip_audio = args.video_only
         start_time = time.time()
         
+        # Determine YOLO usage: explicit flags override auto-detection
+        if args.no_yolo:
+            use_yolo_setting = False
+        elif args.use_yolo:
+            use_yolo_setting = True
+        else:
+            use_yolo_setting = None  # Let main() auto-detect based on directory
+        
         if skip_audio:
             print("🚀 VIDEO-ONLY MODE: Fast video classification and play analysis")
             print("=" * 70)
@@ -327,7 +404,8 @@ if __name__ == "__main__":
             transcript_file=args.transcript_file,
             play_analysis_file=args.play_analysis_file,
             skip_audio=skip_audio,
-            max_clips=args.max_clips
+            max_clips=args.max_clips,
+            use_yolo=use_yolo_setting
         )
         
         elapsed = time.time() - start_time

speed_test.py

@@ -7,6 +7,7 @@ import time
 import os
 import glob
 from inference import predict_clip, transcribe_clip, analyze_play_state
+from config import DEFAULT_DATA_DIR
 
 def test_single_clip_speed(clip_path: str, num_runs: int = 5):
     """Test speed of processing a single clip multiple times"""
@@ -53,7 +54,7 @@ def test_single_clip_speed(clip_path: str, num_runs: int = 5):
     
     return avg_video, avg_audio, avg_total
 
-def test_batch_speed(input_dir: str = "data", max_clips: int = 10):
+def test_batch_speed(input_dir: str = DEFAULT_DATA_DIR, max_clips: int = 10):
     """Test speed of batch processing"""
     print(f"\n=== Batch Processing Speed Test ===")
     
@@ -101,7 +102,7 @@ def test_batch_speed(input_dir: str = "data", max_clips: int = 10):
     
     return total_time, avg_clip_time
 
-def test_pipeline_modes(input_dir: str = "data", max_clips: int = 5):
+def test_pipeline_modes(input_dir: str = DEFAULT_DATA_DIR, max_clips: int = 5):
     """Compare different pipeline processing modes"""
     print(f"\n=== Pipeline Mode Comparison ===")
     

video.py

@@ -27,7 +27,7 @@ from config import (
     KINETICS_LABELS_URL, KINETICS_LABELS_PATH, VIDEO_MEAN, VIDEO_STD,
     PLAY_CONFIDENCE_THRESHOLD, PLAY_BOUNDARY_WINDOW_SIZE,
     PLAY_START_INDICATORS, PLAY_ACTION_INDICATORS, NON_PLAY_INDICATORS,
-    ENABLE_DEBUG_PRINTS, ENABLE_FRAME_SHAPE_DEBUG
+    ENABLE_DEBUG_PRINTS, ENABLE_FRAME_SHAPE_DEBUG, TORCH_HUB_CACHE_DIR
 )
 
 
@@ -61,11 +61,22 @@ class VideoClassifier:
         if ENABLE_DEBUG_PRINTS:
             print(f"Loading X3D model: {self.model_name}")
         
-        self.model = torch.hub.load(
-            "facebookresearch/pytorchvideo",
-            self.model_name,
-            pretrained=True
-        ).to(self.device).eval()
+        # Set torch hub cache directory if configured
+        original_cache_dir = None
+        if TORCH_HUB_CACHE_DIR:
+            original_cache_dir = torch.hub.get_dir()
+            torch.hub.set_dir(TORCH_HUB_CACHE_DIR)
+        
+        try:
+            self.model = torch.hub.load(
+                "facebookresearch/pytorchvideo",
+                self.model_name,
+                pretrained=True
+            ).to(self.device).eval()
+        finally:
+            # Restore original cache directory if it was changed
+            if original_cache_dir is not None:
+                torch.hub.set_dir(original_cache_dir)
         
         if ENABLE_DEBUG_PRINTS:
             print(f"Model loaded successfully on {self.device}")

yolo_processor.py

@@ -0,0 +1,327 @@
+"""
+YOLO11 Football Object Detection and Video Preprocessing Module.
+
+This module integrates YOLOv11-based object detection into the NFL play detection pipeline.
+It processes raw video clips to add bounding box annotations for football players, balls,
+and other sports objects before video classification.
+
+Key Components:
+- YOLOProcessor: Main class for YOLO-based video preprocessing
+- FootballDetector: Football-specific object detection functionality
+- VideoAnnotator: Video annotation and output management
+
+Integration Flow:
+1. Raw clips in /segments/ are processed by YOLO11
+2. Annotated clips are saved to /segments/yolo/
+3. Video classification uses annotated clips
+4. Audio transcription continues to use original clips
+"""
+
+import os
+import shutil
+import tempfile
+import subprocess
+import glob
+from typing import Optional, Tuple, List
+from pathlib import Path
+
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+from config import (
+    ENABLE_DEBUG_PRINTS, DEFAULT_SEGMENTS_DIR, DEFAULT_YOLO_OUTPUT_DIR,
+    DEFAULT_TEMP_DIR, HUGGINGFACE_CACHE_DIR
+)
+
+
+class YOLOProcessor:
+    """
+    YOLOv11-based video processor for football object detection.
+    
+    Downloads and manages YOLOv11 models, processes video clips to add
+    object detection annotations, and manages output directories.
+    """
+    
+    def __init__(self, model_size: str = "nano", confidence_threshold: float = 0.25):
+        """
+        Initialize YOLO processor.
+        
+        Args:
+            model_size: YOLO model size (nano, small, medium, large, xlarge)
+            confidence_threshold: Detection confidence threshold
+        """
+        self.model_size = model_size
+        self.confidence_threshold = confidence_threshold
+        self.model = None
+        self._load_model()
+    
+    def _load_model(self) -> None:
+        """Download and load YOLOv11 model."""
+        model_filename = f"yolo11{self.model_size[0]}.pt"  # nano -> yolo11n.pt
+        
+        if ENABLE_DEBUG_PRINTS:
+            print(f"Loading YOLOv11 model: {model_filename}")
+        
+        try:
+            # Set cache directory if configured
+            download_kwargs = {
+                "repo_id": "Ultralytics/YOLO11",
+                "filename": model_filename,
+                "repo_type": "model"
+            }
+            if HUGGINGFACE_CACHE_DIR:
+                download_kwargs["cache_dir"] = HUGGINGFACE_CACHE_DIR
+            
+            model_path = hf_hub_download(**download_kwargs)
+            self.model = YOLO(model_path)
+            
+            if ENABLE_DEBUG_PRINTS:
+                print(f"YOLOv11 model loaded successfully from {model_path}")
+                
+        except Exception as e:
+            print(f"[ERROR] Failed to load YOLOv11 model: {e}")
+            raise
+    
+    def process_clip(self, input_path: str, output_dir: str) -> Optional[str]:
+        """
+        Process a single video clip with YOLO object detection.
+        
+        Args:
+            input_path: Path to input video clip
+            output_dir: Directory for output annotated clip
+            
+        Returns:
+            Path to annotated clip with audio, or None if processing failed
+        """
+        if not os.path.exists(input_path):
+            print(f"[ERROR] Input video not found: {input_path}")
+            return None
+        
+        # Create output directory if it doesn't exist
+        os.makedirs(output_dir, exist_ok=True)
+        
+        # Create temporary directory for processing
+        temp_dir = DEFAULT_TEMP_DIR if DEFAULT_TEMP_DIR else None
+        with tempfile.TemporaryDirectory(dir=temp_dir) as tmp_dir:
+            try:
+                return self._process_in_temp_dir(input_path, output_dir, tmp_dir)
+            except Exception as e:
+                print(f"[ERROR] Failed to process {input_path}: {e}")
+                return None
+    
+    def _process_in_temp_dir(self, input_path: str, output_dir: str, tmp_dir: str) -> str:
+        """Process video in temporary directory and return final output path."""
+        base_name = os.path.basename(input_path)
+        name_without_ext = os.path.splitext(base_name)[0]
+        _, ext = os.path.splitext(base_name)
+        
+        # Stage input file in temp directory
+        temp_input = os.path.join(tmp_dir, base_name)
+        shutil.copy(input_path, temp_input)
+        
+        # Run YOLO inference
+        if ENABLE_DEBUG_PRINTS:
+            print(f"Running YOLO inference on {base_name}")
+        
+        self.model.predict(
+            source=temp_input,
+            imgsz=640,
+            conf=self.confidence_threshold,
+            save=True,
+            project=tmp_dir,
+            name="yolo_out",
+            exist_ok=True,
+            verbose=False  # Reduce YOLO output noise
+        )
+        
+        # Find the annotated output
+        yolo_out_dir = os.path.join(tmp_dir, "yolo_out")
+        annotated_files = glob.glob(os.path.join(yolo_out_dir, "*"))
+        
+        if not annotated_files:
+            raise FileNotFoundError(f"No YOLO output found in {yolo_out_dir}")
+        
+        # Get the largest file (should be the annotated video)
+        annotated_video = max(annotated_files, key=lambda f: os.path.getsize(f))
+        
+        # Create final output path
+        final_output = os.path.join(output_dir, f"{name_without_ext}_yolo{ext}")
+        
+        # Mux original audio with annotated video using FFmpeg
+        self._mux_audio(annotated_video, input_path, final_output)
+        
+        if ENABLE_DEBUG_PRINTS:
+            print(f"YOLO processing complete: {final_output}")
+        
+        return final_output
+    
+    def _mux_audio(self, video_path: str, audio_source: str, output_path: str) -> None:
+        """
+        Combine annotated video with original audio using FFmpeg.
+        
+        Args:
+            video_path: Path to annotated video (without audio)
+            audio_source: Path to original video (with audio)
+            output_path: Path for final output with both video and audio
+        """
+        cmd = [
+            "ffmpeg", "-y",  # Overwrite output file
+            "-i", video_path,     # Annotated video input
+            "-i", audio_source,   # Original audio source
+            "-map", "0:v:0",      # Map video from first input
+            "-map", "1:a:0",      # Map audio from second input
+            "-c:v", "copy",       # Copy video without re-encoding
+            "-c:a", "copy",       # Copy audio without re-encoding
+            output_path
+        ]
+        
+        try:
+            subprocess.run(
+                cmd, 
+                check=True, 
+                stdout=subprocess.DEVNULL, 
+                stderr=subprocess.DEVNULL
+            )
+        except subprocess.CalledProcessError as e:
+            raise RuntimeError(f"FFmpeg audio muxing failed: {e}")
+
+
+class FootballDetector:
+    """
+    High-level interface for football-specific object detection pipeline.
+    
+    Manages the integration between raw video clips and the NFL play detection
+    system by preprocessing clips with YOLO object detection.
+    """
+    
+    def __init__(self, 
+                 segments_dir: str = DEFAULT_SEGMENTS_DIR,
+                 yolo_output_dir: str = DEFAULT_YOLO_OUTPUT_DIR,
+                 model_size: str = "nano",
+                 confidence: float = 0.25):
+        """
+        Initialize football detector.
+        
+        Args:
+            segments_dir: Directory containing raw video segments
+            yolo_output_dir: Directory for YOLO-processed clips
+            model_size: YOLO model size for detection
+            confidence: Detection confidence threshold
+        """
+        self.segments_dir = segments_dir
+        self.yolo_output_dir = yolo_output_dir
+        self.processor = YOLOProcessor(model_size=model_size, confidence_threshold=confidence)
+        
+        # Ensure output directory exists
+        os.makedirs(self.yolo_output_dir, exist_ok=True)
+    
+    def process_all_segments(self, max_clips: Optional[int] = None) -> List[str]:
+        """
+        Process all video segments in the segments directory.
+        
+        Args:
+            max_clips: Maximum number of clips to process (for testing)
+            
+        Returns:
+            List of paths to processed YOLO clips
+        """
+        # Find all video files in segments directory
+        video_patterns = ["*.mov", "*.mp4"]
+        video_files = []
+        
+        for pattern in video_patterns:
+            video_files.extend(glob.glob(os.path.join(self.segments_dir, pattern)))
+        
+        video_files = sorted(video_files)
+        
+        if max_clips:
+            video_files = video_files[:max_clips]
+        
+        if not video_files:
+            print(f"No video files found in {self.segments_dir}")
+            return []
+        
+        print(f"🎯 YOLO PREPROCESSING: Processing {len(video_files)} clips")
+        print("=" * 60)
+        
+        processed_clips = []
+        
+        for i, video_path in enumerate(video_files, 1):
+            clip_name = os.path.basename(video_path)
+            print(f"[{i}/{len(video_files)}] Processing: {clip_name}")
+            
+            # Check if already processed
+            name_without_ext = os.path.splitext(clip_name)[0]
+            _, ext = os.path.splitext(clip_name)
+            expected_output = os.path.join(self.yolo_output_dir, f"{name_without_ext}_yolo{ext}")
+            
+            if os.path.exists(expected_output):
+                print(f"  ✓ Already processed: {expected_output}")
+                processed_clips.append(expected_output)
+                continue
+            
+            # Process with YOLO
+            output_path = self.processor.process_clip(video_path, self.yolo_output_dir)
+            
+            if output_path:
+                processed_clips.append(output_path)
+                print(f"  ✓ YOLO annotations added: {os.path.basename(output_path)}")
+            else:
+                print(f"  ✗ Processing failed for {clip_name}")
+        
+        print(f"\n🎯 YOLO PREPROCESSING COMPLETE")
+        print(f"  Processed clips: {len(processed_clips)}")
+        print(f"  Output directory: {self.yolo_output_dir}")
+        
+        return processed_clips
+    
+    def get_processed_clip_path(self, original_clip_path: str) -> Optional[str]:
+        """
+        Get the path to the YOLO-processed version of a clip.
+        
+        Args:
+            original_clip_path: Path to original clip
+            
+        Returns:
+            Path to YOLO-processed clip, or None if not found
+        """
+        clip_name = os.path.basename(original_clip_path)
+        name_without_ext = os.path.splitext(clip_name)[0]
+        _, ext = os.path.splitext(clip_name)
+        
+        yolo_clip_path = os.path.join(self.yolo_output_dir, f"{name_without_ext}_yolo{ext}")
+        
+        return yolo_clip_path if os.path.exists(yolo_clip_path) else None
+
+
+# ============================================================================
+# CONVENIENCE FUNCTIONS
+# ============================================================================
+
+def preprocess_segments_with_yolo(segments_dir: str = DEFAULT_SEGMENTS_DIR, 
+                                 max_clips: Optional[int] = None) -> List[str]:
+    """
+    Convenience function to preprocess all segments with YOLO detection.
+    
+    Args:
+        segments_dir: Directory containing video segments
+        max_clips: Maximum clips to process (for testing)
+        
+    Returns:
+        List of paths to YOLO-processed clips
+    """
+    detector = FootballDetector(segments_dir=segments_dir)
+    return detector.process_all_segments(max_clips=max_clips)
+
+def get_yolo_clip_for_original(original_path: str) -> Optional[str]:
+    """
+    Get YOLO-processed version of an original clip.
+    
+    Args:
+        original_path: Path to original clip
+        
+    Returns:
+        Path to YOLO-processed clip or None
+    """
+    detector = FootballDetector()
+    return detector.get_processed_clip_path(original_path)