My First YOLO Adventure

Have you ever wondered what's wandering through your backyard at night? Trail cameras are amazing for capturing wildlife, but they generate thousands of photos—most of them empty forest shots triggered by wind or shadows. After getting my first trail cam, I quickly realized I needed a better solution than manually sorting through endless photos of swaying branches.

That's when I discovered YOLO (You Only Look Once), a computer vision model that can detect objects in real-time. This became my first serious dive into computer vision, and I learned some fascinating lessons along the way. Let me share the journey and some interesting challenges others might find useful.

Of course I had to make a viewer ☝️ for the application, and an MDX version for this post, more on that later!

The Problem: Too Many Photos, Too Little Time

If you are not familiar, trail cameras are motion-activated, but they're not very smart. A branch moving in the wind? Click. A shadow shifting? Click. An actual deer? Click. You end up with thousands of photos where maybe 5-10% actually contain wildlife.

My goal was simple: automatically detect and organize photos that actually contain animals, saving both storage space and my sanity.

The Solution: YOLO + Python = Magic

The core idea is straightforward:

1. Run each photo through a YOLO model to detect animals
2. Extract the date from the photo's EXIF data
3. Rename and organize photos based on what animals were found
4. Only keep photos with actual wildlife (configurable)

Here's what the final output is supposed to look like:

2024-01-15_14-30-22_deer_raccoon.jpg
2024-01-15_18-45-12_bird.jpg
2024-01-16_02-15-33_no_wildlife.jpg

Interesting Challenges & Solutions

Challenge: EXIF Date Extraction - When Cameras Lie

Trail cameras embed the date/time when each photo was taken in the EXIF metadata. Sounds simple, right? Not so fast.

def get_image_date(self, image_path):
    """Extract date from image EXIF data or file modification time"""
    try:
        # Try to get date from EXIF
        with Image.open(image_path) as img:
            exif_data = img._getexif()
            if exif_data:
                for tag, value in exif_data.items():
                    tag_name = TAGS.get(tag, tag)
                    if tag_name == 'DateTime':
                        return datetime.strptime(value, '%Y:%m:%d %H:%M:%S')
    except Exception as e:
        print(f"EXIF error for {image_path}: {e}")
    
    # Fallback to file modification time
    timestamp = os.path.getmtime(image_path)
    return datetime.fromtimestamp(timestamp)

The Problem: EXIF data is surprisingly unreliable. Some cameras don't include it, others format it differently, and sometimes the data gets corrupted during file transfers.

The Learning: Always have a fallback plan. The file's modification time isn't as accurate as the EXIF timestamp, but it's better than crashing the entire program.

Challenge: The Wildlife Classes Problem

YOLO is trained on the COCO dataset, which includes 80 classes. But which ones count as "wildlife" for a trail cam?

self.wildlife_classes = {
    'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 
    'bear', 'zebra', 'giraffe', 'animal', 'person'  # person included to filter humans
}

The Interesting Decision: I included 'person' in the wildlife classes, because I wanted to know if people were on the property! This shows how the same detection model can serve different purposes.

The Expandability: The beauty of this approach is that you can easily customize which animals you care about. Urban setting? Maybe focus on cats, dogs, and birds. Rural farm? Add livestock like sheep and cows. I added 'animal' to mine as a fallback and that is what most of my hits are, it recognized something was there but could not classify, if given more time I could add some training data for myself in Roboflow to augment the COCO dadtaset.

Challenge: Configuration Management - Making It User-Friendly

Rather than hardcoding settings, I created a configuration system that others can easily modify:

# examples/sample_config.py
INPUT_DIR = "trail_cam_photos"
OUTPUT_DIR = "processed_wildlife"
CONFIDENCE_THRESHOLD = 0.3
SAVE_ALL_PHOTOS = False  # Only save photos with wildlife

The User Experience: Copy the sample config, update your folder paths, and you're ready to go. No need to dig through the main script to change settings.

The Developer Lesson: Good configuration management makes your tool accessible to non-programmers and easier to maintain.

The Results: From Chaos to Organization

After running this on my first batch of 2,847 trail cam photos:

• Total processed: 2,847 photos
• Wildlife found: 284 photos (10% hit rate)
• Animals detected: deer (156), bird (89), raccoon (31), cat (8)
• Storage saved: ~85% by only keeping wildlife photos

Key Takeaways

1. Start Simple: My first version just detected animals and printed results. The file organization came later.

2. Configuration Over Hardcoding: Making settings configurable from day one saves massive refactoring later.

3. Test on Real Data: YOLO tutorials often use clean, well-lit photos. Trail cam photos are grainy, dark, and challenging.

4. Performance Matters: When you're processing thousands of photos, every second counts, the smaller nano YOLO works great to save time

What's Next?

This project opened up computer vision possibilities:

• Adding custom animal classes trained on my specific area.
• Automated processing of images when I move them into a folder on by drive.
• This project was really for me to get familar with some OOTB CV models for my real use case in AEC (Architectiure Engineering and Construction) applications!

Try It Yourself

The entire project is MIT licensed and available on GitHub. The setup is straightforward:

# Setup
./setup.sh

# Configure
cp examples/sample_config.py config.py
# Edit config.py with your paths

# Test single image
python test_single_image.py sample_photo.jpg

# Process all photos
python wildlife_processor.py

Computer vision seemed intimidating, but with tools like YOLO and libraries like Ultralytics, anyone can build practical solutions with surprisingly little code. Whether you're dealing with trail cam photos, construction cameras, or just want to automatically organize your vacation pictures, the principles are the same: detect, classify, and organize.