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README.md

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WARNING: DO NOT EDIT THIS TABLE MANUALLY. IT IS AUTOMATICALLY GENERATED.
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HEAD OVER TO CONTRIBUTING.MD FOR MORE DETAILS ON HOW TO MAKE CHANGES PROPERLY.
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## 🚀 model tutorials (50 notebooks)
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## 🚀 model tutorials (51 notebooks)
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| **notebook** | **open in colab / kaggle / sagemaker studio lab** | **complementary materials** | **repository / paper** |
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|:------------:|:-------------------------------------------------:|:---------------------------:|:----------------------:|
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| [Zero-Shot Object Detection and Segmentation with Google Gemini 2.5](https://github.com/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-and-segmentation-with-google-gamini-2-5.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-and-segmentation-with-google-gamini-2-5.ipynb) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-and-segmentation-with-google-gamini-2-5.ipynb) | | [![arXiv](https://img.shields.io/badge/arXiv-2507.06261-b31b1b.svg)](https://arxiv.org/abs/2507.06261)|
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| [Fine-Tune RF-DETR on Object Detection Dataset](https://github.com/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-rf-detr-on-detection-dataset.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-rf-detr-on-detection-dataset.ipynb) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-rf-detr-on-detection-dataset.ipynb) | [![Roboflow](https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg)](https://blog.roboflow.com/rf-detr) [![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/live/yHW0ip-2i54) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/roboflow/rf-detr) |
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| [Zero-Shot Object Detection and Segmentation with YOLOE](https://github.com/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-and-segmentation-with-yoloe.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-and-segmentation-with-yoloe.ipynb) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-and-segmentation-with-yoloe.ipynb) | [![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/watch?v=eHAnIehnCt4) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/THU-MIG/yoloe) [![arXiv](https://img.shields.io/badge/arXiv-2503.07465-b31b1b.svg)](https://arxiv.org/abs/2503.07465)|
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| [Fine-Tune YOLOv12 on Object Detection Dataset](https://github.com/roboflow-ai/notebooks/blob/main/notebooks/train-yolov12-object-detection-model.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov12-object-detection-model.ipynb) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/roboflow-ai/notebooks/blob/main/notebooks/train-yolov12-object-detection-model.ipynb) | [![Roboflow](https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg)](https://blog.roboflow.com/train-yolov12-model) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/sunsmarterjie/yolov12) [![arXiv](https://img.shields.io/badge/arXiv-2502.12524-b31b1b.svg)](https://arxiv.org/abs/2502.12524)|

automation/notebooks-table-data.csv

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display_name, notebook_name, roboflow_blogpost_path, youtube_video_path, github_repository_path, arxiv_index, should_open_in_sagemaker_labs, readme_section
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Zero-Shot Object Detection and Segmentation with Google Gemini 2.5, zero-shot-object-detection-and-segmentation-with-google-gamini-2-5.ipynb, , , ,2507.06261, False, models
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How to Track Objects with SORT Tracker, how-to-track-objects-with-sort-tracker.ipynb, , , https://github.com/roboflow/trackers, 1602.00763, False, trackers
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How to Track Objects with DeepSORT Tracker, how-to-track-objects-with-deepsort-tracker.ipynb, , , https://github.com/roboflow/trackers, 1703.07402, False, trackers
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Fine-Tune RF-DETR on Object Detection Dataset, how-to-finetune-rf-detr-on-detection-dataset.ipynb, https://blog.roboflow.com/rf-detr, https://www.youtube.com/live/yHW0ip-2i54, https://github.com/roboflow/rf-detr, , False, models

notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb

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"\n",
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"\n",
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"[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb)\n",
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"[![Roboflow](https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg)](https://blog.roboflow.com/florence-2/)\n",
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"[![arXiv](https://img.shields.io/badge/arXiv-2311.06242-b31b1b.svg)](https://arxiv.org/abs/2311.06242)\n",
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"\n",
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"Florence-2 is a lightweight vision-language model open-sourced by Microsoft under the MIT license. The model demonstrates strong zero-shot and fine-tuning capabilities across tasks such as captioning, object detection, grounding, and segmentation.\n",
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"![Florence-2 Figure.1](https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/florence-2-figure-1.png)\n",
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"*Figure 1. Illustration showing the level of spatial hierarchy and semantic granularity expressed by each task. Source: Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks.*\n",
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"The model takes images and task prompts as input, generating the desired results in text format. It uses a DaViT vision encoder to convert images into visual token embeddings. These are then concatenated with BERT-generated text embeddings and processed by a transformer-based multi-modal encoder-decoder to generate the response.\n",
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"![Florence-2 Figure.2](https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/florence-2-figure-2.png)\n",
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"*Figure 2. Overview of Florence-2 architecture. Source: Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks.*\n",
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"![Florence-2 Figure.1](https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/florence-2-figure-1.png)\n",
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"\n"
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],
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"metadata": {
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{
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"cell_type": "markdown",
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"source": [
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"# Congratulations\n",
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"<div align=\"center\">\n",
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" <p>\n",
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" Looking for more tutorials or have questions?\n",
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" Check out our <a href=\"https://github.com/roboflow/notebooks\">GitHub repo</a> for more notebooks,\n",
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" or visit our <a href=\"https://discord.gg/GbfgXGJ8Bk\">discord</a>.\n",
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" </p>\n",
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" \n",
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" <p>\n",
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" <strong>If you found this helpful, please consider giving us a ⭐\n",
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" <a href=\"https://github.com/roboflow/notebooks\">on GitHub</a>!</strong>\n",
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" </p>\n",
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"\n",
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"⭐️ If you enjoyed this notebook, [**star the Roboflow Notebooks repo**](https://https://github.com/roboflow/notebooks) (and [**supervision**](https://github.com/roboflow/supervision) while you're at it) and let us know what tutorials you'd like to see us do next. ⭐️"
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"</div>"
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],
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"metadata": {
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"id": "ag0XROk7fcd_"

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