---
base_model: microsoft/Florence-2-base-ft
library_name: peft
widget:
  - src: >-
      https://source.roboflow.com/lfoemouH7BfQ20xsrtlOyKT2ANP2/KVoWCn5BOS05traUI56l/original.jpg
    example_title: Data set image 0921
---

# Model Card for Model ID

A fine tuning of florence 2 using the [robocelesc](https://universe.roboflow.com/testerobocelesc/robocelesc-zrn9m) dataset from roboflow for identifying components on a utility pole.



## Model Details

### Model Description

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- **License:** MIT
- **Finetuned from model [optional]:** microsoft/Florence-2-base-ft

### Model Sources [optional]

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## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Results

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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@misc{
robocelesc-zrn9m_dataset,
title = { robocelesc Dataset },
type = { Open Source Dataset },
author = { testerobocelesc },
howpublished = { \url{ https://universe.roboflow.com/testerobocelesc/robocelesc-zrn9m } },
url = { https://universe.roboflow.com/testerobocelesc/robocelesc-zrn9m },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { jun },
note = { visited on 2024-08-08 },
}

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.12.0