Roboflow | Florence-2: Fine-tune Microsoft’s Multimodal Model @Roboflow | Uploaded 3 months ago | Updated 1 hour ago
Learn how to fine-tune Microsoft's Florence-2, a powerful open-source Vision Language Model, for custom object detection tasks. This in-depth tutorial guides you through setting up your environment in Google Colab, preparing datasets, and optimizing the model using LoRA.
Chapters:
- 00:00 Introduction: Unlock the Power of Florence-2
- 01:09 Getting Started: Prepare for VLM Fine-Tuning
- 03:55 Florence-2 in Action: Explore Pre-trained Capabilities
- 07:00 Dataset Deep Dive: PyTorch Data Loading for Florence-2
- 13:02 LoRA: Optimize Your VLM Training
- 14:21 Fine-Tuning: Unleash Florence-2's Custom Object Detection
- 17:30 Model Evaluation: Measure Your VLM's Success
- 21:37 Florence-2 vs Other Computer Vision Models
- 24:09 Conclusion and Next Steps
Resources:
- Roboflow: roboflow.com
- 🔴 Community Session July 3th, 2024 at 08:00 AM PST / 11:00 AM EST / 05:00 PM CET: https://roboflow.stream
- ⭐ Notebooks GitHub: github.com/roboflow/notebooks
- 📓 Florence notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb
- 🗞 Florence-2 arXiv paper: arxiv.org/abs/2311.06242
- 🗞 Florence-2 overview blog post: blog.roboflow.com/florence-2
- 🗞 Florence-2 fine-tuning blog post: blog.roboflow.com/fine-tune-florence-2-object-detection
- 🔗 Florence-2 HF Space: huggingface.co/spaces/gokaygokay/Florence-2
- 🗞 Mean Average Precision (mAP) blog post: blog.roboflow.com/mean-average-precision
- 🗞 Confusion Matrix blog post: blog.roboflow.com/what-is-a-confusion-matrix
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
Learn how to fine-tune Microsoft's Florence-2, a powerful open-source Vision Language Model, for custom object detection tasks. This in-depth tutorial guides you through setting up your environment in Google Colab, preparing datasets, and optimizing the model using LoRA.
Chapters:
- 00:00 Introduction: Unlock the Power of Florence-2
- 01:09 Getting Started: Prepare for VLM Fine-Tuning
- 03:55 Florence-2 in Action: Explore Pre-trained Capabilities
- 07:00 Dataset Deep Dive: PyTorch Data Loading for Florence-2
- 13:02 LoRA: Optimize Your VLM Training
- 14:21 Fine-Tuning: Unleash Florence-2's Custom Object Detection
- 17:30 Model Evaluation: Measure Your VLM's Success
- 21:37 Florence-2 vs Other Computer Vision Models
- 24:09 Conclusion and Next Steps
Resources:
- Roboflow: roboflow.com
- 🔴 Community Session July 3th, 2024 at 08:00 AM PST / 11:00 AM EST / 05:00 PM CET: https://roboflow.stream
- ⭐ Notebooks GitHub: github.com/roboflow/notebooks
- 📓 Florence notebook: colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb
- 🗞 Florence-2 arXiv paper: arxiv.org/abs/2311.06242
- 🗞 Florence-2 overview blog post: blog.roboflow.com/florence-2
- 🗞 Florence-2 fine-tuning blog post: blog.roboflow.com/fine-tune-florence-2-object-detection
- 🔗 Florence-2 HF Space: huggingface.co/spaces/gokaygokay/Florence-2
- 🗞 Mean Average Precision (mAP) blog post: blog.roboflow.com/mean-average-precision
- 🗞 Confusion Matrix blog post: blog.roboflow.com/what-is-a-confusion-matrix
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
