Readme.md

---
license: apache-2.0
base_model: ultralytics/yolov8m
tags:
- computer-vision
- object-detection
- yolov8
- logo-detection
- ultralytics
- roboflow
pipeline_tag: object-detection
library_name: ultralytics
datasets:
- roboflow
---

# Logo Detection Model - ComputerVisionF5

This model detects and localizes 6 specific logos in images: **F5**, **Factoria**, **FemCoders**, **Fundacion Orange**, **Microsoft**, and **SomosF5**. Built on YOLOv8m architecture for real-time object detection with high accuracy.

## Model Details

### Model Description

This is a fine-tuned YOLOv8m model specialized for logo detection and recognition. The model can identify and locate 6 different organizational logos within images, providing bounding boxes with confidence scores for each detection.

- **Developed by:** Mariden
- **Model type:** Object Detection (Logo Recognition)
- **License:** Apache-2.0  
- **Base model:** ultralytics/yolov8m
- **Dataset:** Custom dataset created with Roboflow
- **Classes:** 6 logo categories

### Model Sources

- **Repository:** https://huggingface.co/Mariden/ComputerVisionF5
- **Base Model:** https://github.com/ultralytics/ultralytics
- **Dataset Platform:** https://roboflow.com

## Uses

### Direct Use

The model is designed for:
- **Brand monitoring** and recognition systems
- **Content analysis** and automated tagging
- **Marketing analytics** and logo tracking
- **Educational projects** for computer vision

### Downstream Use

Can be integrated into:
- Brand monitoring and social media analysis tools
- Content management and digital asset systems  
- Marketing analytics dashboards
- Automated image classification pipelines
- Real-time logo detection applications

### Out-of-Scope Use

- General object detection (optimized specifically for these 6 logos)
- Real-time video processing without adequate hardware
- Detection of logos not present in the training dataset
- Commercial use without proper attribution

## How to Get Started with the Model

### Using Hugging Face Transformers (Recommended)

```python
from transformers import pipeline
from PIL import Image

# Load the detection pipeline
detector = pipeline("object-detection", model="Mariden/ComputerVisionF5")

# Load and process an image
image = Image.open("path/to/your/image.jpg")
results = detector(image)

# Display results
for result in results:
    print(f"Logo: {result['label']}")
    print(f"Confidence: {result['score']:.3f}")
    print(f"Bounding box: {result['box']}")
    print("---")
```

### Using Ultralytics YOLO

```python
from ultralytics import YOLO
from PIL import Image

# Load the model
model = YOLO('Mariden/ComputerVisionF5')

# Run inference
results = model("path/to/your/image.jpg")

# Display results
results[0].show()

# Get detailed predictions
for result in results:
    boxes = result.boxes
    for box in boxes:
        class_name = model.names[int(box.cls)]
        confidence = box.conf.item()
        print(f"Detected: {class_name} ({confidence:.3f})")
```

### Using Inference API

```python
import requests
from PIL import Image
import io

API_URL = "https://api-inference.huggingface.co/models/Mariden/ComputerVisionF5"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def detect_logos(image_path):
    # Load and prepare image
    with open(image_path, "rb") as f:
        data = f.read()
    
    # Make API request
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

# Use the function
results = detect_logos("path/to/your/image.jpg")
print(results)
```

### Batch Processing

```python
from transformers import pipeline
import os

detector = pipeline("object-detection", model="Mariden/ComputerVisionF5")

def process_images_folder(folder_path):
    results = {}
    for filename in os.listdir(folder_path):
        if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
            image_path = os.path.join(folder_path, filename)
            detections = detector(image_path)
            results[filename] = detections
    return results

# Process all images in a folder
batch_results = process_images_folder("path/to/images/")
```

## Detected Logo Classes

| Class ID | Logo Name | Description |
|----------|-----------|-------------|
| 0 | **F5** | Technology and networking company |
| 1 | **Factoria** | Educational technology institution |  
| 2 | **FemCoders** | Women in tech coding bootcamp |
| 3 | **Fundacion Orange** | Digital inclusion foundation |
| 4 | **Microsoft** | Global technology corporation |
| 5 | **SomosF5** | Educational platform and community |

## Model Performance

### Key Features
- **Input Size:** 640x640 pixels (automatically resized)
- **Output:** Bounding boxes with confidence scores
- **Multi-detection:** Can detect multiple logos in single image
- **Real-time capable:** Optimized for fast inference
- **Format Support:** JPEG, PNG, WebP, and other common formats

### Usage Tips
1. **Image Quality:** Works best with clear, well-lit images
2. **Logo Size:** Optimal performance with logos >32x32 pixels
3. **Confidence Threshold:** Default 0.5, adjust based on use case
4. **Multiple Logos:** Efficiently handles multiple logo detections
5. **Aspect Ratios:** Automatically handles different image proportions

## Training Details

### Training Data
- **Source:** Custom dataset created using Roboflow platform
- **Annotation:** Manual bounding box annotation for all 6 logo classes
- **Augmentation:** Applied through Roboflow (rotation, scaling, brightness, etc.)
- **Quality Control:** Manually reviewed and validated annotations

### Training Process
- **Base Model:** YOLOv8m pretrained on COCO dataset  
- **Fine-tuning:** Transfer learning on custom logo dataset
- **Framework:** Ultralytics YOLOv8 training pipeline
- **Optimization:** Mixed precision training for efficiency

### Technical Specifications

#### Architecture
- **Model:** YOLOv8m (Medium variant)
- **Backbone:** CSPDarknet with PANet neck
- **Head:** YOLO detection head with anchor-free design
- **Parameters:** ~25M parameters
- **Input:** RGB images, 640x640 resolution
- **Output:** Bounding boxes, class probabilities, confidence scores

#### Performance Characteristics
- **Speed:** ~50-100 FPS on GPU (depending on hardware)
- **Memory:** ~4GB VRAM for inference
- **Formats:** PyTorch (.pt), ONNX (.onnx) available
- **Deployment:** CPU/GPU compatible

## Model Formats and Files

### Available Formats
- **PyTorch:** `pytorch_model.bin` - Main format for Hugging Face integration
- **ONNX:** `model.onnx` - Optimized for cross-platform deployment
- **Config:** `config.json` - Model configuration and class mappings
- **Preprocessor:** `preprocessor_config.json` - Image preprocessing parameters

### File Structure
```
Mariden/ComputerVisionF5/
├── model.pt                   # Main PyTorch model
├── model.onnx                 # ONNX optimized version  
├── config.json                # Model configuration
├── preprocessor_config.json   # Image preprocessing config
└── README.md                  # This documentation
```

## Limitations and Considerations

### Current Limitations
- **Scope:** Only detects the specific 6 trained logo classes
- **Variations:** Performance may vary with logo design changes
- **Size Constraints:** Very small logos (<32px) may not be detected reliably
- **Occlusion:** Partially hidden logos might be missed
- **Lighting:** Extreme lighting conditions may affect accuracy

### Best Practices
- Use high-resolution images when possible
- Ensure good lighting and contrast
- Avoid heavily compressed images
- Test confidence thresholds for your specific use case
- Consider image preprocessing for challenging conditions

## Ethical Considerations

This model is designed for educational and research purposes. When using for commercial applications:
- Ensure proper licensing and attribution
- Respect trademark and copyright policies
- Consider privacy implications when processing user-generated content
- Use responsibly for brand monitoring and analysis

## Citation and Attribution

If you use this model in your research or applications, please cite:

```bibtex
@model{mariden2024logodetection,
  title={Logo Detection Model - ComputerVisionF5},
  author={Mariden},
  year={2024},
  publisher={Hugging Face},
  journal={Hugging Face Model Hub},
  howpublished={\url{https://huggingface.co/Mariden/ComputerVisionF5}}
}
```

## Support and Contact

- **Issues:** Please report issues in the Hugging Face model repository
- **Questions:** Use the Community tab for questions and discussions
- **Updates:** Follow the repository for model updates and improvements

---

**License:** Apache-2.0 | **Framework:** Ultralytics YOLOv8 | **Platform:** Hugging Face 🤗