---
license: apache-2.0
base_model:
- timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k
library_name: fastai
tags:
- Coleoptera
- Taxonomy
- Biology
- Cicindelidae
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model was trained on all specimens of *Cicindela* tiger beetles from the Field Museum. It is a multilabel model able to identify species and subspecies. See the model config file for all labels included and the publication for metrics.

## Model Details

This model is based on the pre-trained [eva02_large_patch14_448](https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k) from the timm library.

The training included several tricks to allow multilabel training with an imbalanced dataset, see the publication for details.


### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** [XXXX]
- **Model type:** Image classification
- **License:** Apache 2.0
- **Finetuned from model [optional]:** [eva02_large_patch14_448](https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k)

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Paper [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Identification of pinned *Cicindela* specimens.



### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

This model will fail to make predictions on species not present in the FMNH collection. It is also unlikely to perform well for specimens that are not pinned.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The model is only expected to perform well for images of pinned tiger beetles.


## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

Data generated by [XXXX] using DrawerDissect on Field Museum specimens, see the publication for details.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->


#### Training Hyperparameters

- **Training regime:** fp16 mixed precision

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

A subset of the specimens was held as a test set. See publication for details


#### Metrics

Metrics in the test set:

Dataset Overview:
- Total taxa analyzed: 193
- Species: 115 (59.6%)
- Subspecies: 78 (40.4%)

Performance Summary:

Species:
- Specimen-weighted precision: 96.8%
- Specimen-weighted recall: 80.9%
- Specimen-weighted precision: 97.0%
- Specimen-weighted recall: 96.4%

Subspecies:
- Specimen-weighted precision: 89.0%
- Specimen-weighted recall: 66.5%
- Specimen-weighted precision: 85.0%
- Specimen-weighted recall: 89.0%

## Usage

The learner can be loaded to fastai with:

```{python}
from huggingface_hub import from_pretrained_fastai
learn = from_pretrained_fastai("brunoasm/eva02_large_patch14_448.Cicindela_ID_FMNH")
```

To avoid loading a pickle file and loading the model weights only, you can use:
```{python}
import requests
import io
from fastai.vision.all import *

def load_model_from_url(learn, url):
    try:
        print("Downloading model...")
        response = requests.get(url, stream=True)
        response.raise_for_status()
        
        buffer = io.BytesIO(response.content)
        learn.load(buffer, with_opt=False)
        print("Model loaded successfully!")
        
    except Exception as e:
        print(f"Error loading model: {e}")

url = 'https://huggingface.co/brunoasm/eva02_large_patch14_448.Cicindela_ID_FMNH/resolve/main/pytorch_model.bin'
learn = vision_learner(dls, "eva02_large_patch14_448.mim_m38m_ft_in22k_in1k")

response = requests.get(url, stream=True)
response.raise_for_status()
buffer = io.BytesIO(response.content)
learn.load(buffer, with_opt=False)

```
where `dls` is a previously created dataloader.


## Citation 

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

[XXXX]. DrawerDissect: Whole-drawer insect imaging, segmentation, and transcription using AI.

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]


## Model Card Authors

[XXXX].