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---
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
tags:
- object-detection
---
# 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
<!-- Provide a longer summary of what this model is. -->
- **License:** MIT
- **Finetuned from model [optional]:** microsoft/Florence-2-base-ft
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
@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]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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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