yusufbukarmaina/florence2-model

Fine-tune Vision AI Model for Volume Recognition

This project demonstrates how to fine-tune a vision AI model for recognizing fluid volumes in test tubes, with applications across medical, laboratory, and industrial settings.

Prerequisites

1. HuggingFace Setup (Required)

1. Create an account at huggingface.co
2. Go to Settings → Access Tokens
3. Create a new token (read access)
4. Copy and save your token - you'll need it later

Quick Start

1. Open terminal in your JarvisLabs workspace:

   File > New Launcher > Terminal
   

2. Clone the repository:

   git clone https://github.com/ictBioRtc/finetune_florence2_vision_language_model.git
   

3. Navigate to project directory:

   cd finetune_vision_ai_model
   

4. Install dependencies:

   pip install -r requirements.txt
   

5. Run the application:

   python app.py
   

6. Copy the public URL provided (e.g., https://ff20bc33e416f3319f.gradio.live)

7. Open in a new browser tab

Using the Application

Step 1: Test Initial Model (Inference Tab)

1. Unzip the provided test_images.zip
2. Go to "Inference" tab
3. Upload a test image
4. Leave other settings at default
5. Click "Run Inference"
6. Observe how the untrained model performs

Step 2: Train the Model (Training Tab)

1. Dataset: ictbiortc/beaker-volume-recognition-dataset
2. Change epochs to 15 (for workshop purposes)
3. Click "Start Training"
4. Note: Full training could take ~5 hours

Step 3: Upload Model to HuggingFace

1. After training completes, click "Upload to Hub"
2. Enter your model name (e.g., your-username/beaker-volume-recognition-model)
3. Paste your HuggingFace token
4. Click "Upload"

Step 4: Important Configuration Update

1. Go to your model on HuggingFace
2. Navigate to "Files and versions"
3. Find config.json
4. Edit line 165 from:

   "model_type": "",
   

   to:

   "model_type": "davit",
   

Step 5: Evaluate Your Model

1. Return to the app
2. Go to "Evaluate" tab
3. Upload a test image
4. Use your trained model
5. Compare results with the initial inference

Applications

This volume recognition model has potential applications in:

• IV Fluid Monitoring
• Laboratory Automation
• Medication Dosing
• Urine Monitoring
• Manufacturing Quality Control
• Chemical Processing
• Beverage Industry
• Petroleum Industry

Training Notes

• Full training typically takes days
• Workshop version uses 15 epochs (~5 hours)
• Larger epoch numbers yield better results
• GPU acceleration is recommended

Troubleshooting

Common issues:

1. "Model not loading": Check your internet connection
2. "Training too slow": Verify GPU availability
3. "Upload failed": Verify your HuggingFace token
4. "Config error": Double-check the davit model_type update

Next Steps

After successful training:

1. Experiment with different epochs
2. Try different image types
3. Test various fluid volumes
4. Integrate with your specific use case

Congratulations! You've successfully:

1. Tested a base vision model
2. Fine-tuned it for volume recognition
3. Uploaded it to HuggingFace
4. Created a practical AI solution for real-world applications

This workshop demonstrates how vision language models can be adapted for specific industrial and medical applications.