---
tags:
- image-classification
- timm
- transformers
- fastai
library_name: fastai
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-22k
- iloncka/mosquito-species-classification-dataset
metrics:
- accuracy
base_model:
- timm/tiny_vit_21m_224.dist_in22k_ft_in1k
---

# Model Card for `culico-net-cls-v1`

`culico-net-cls-v1` - image classification model focused on identifying mosquito species. This model is a result of the `CulicidaeLab` project and was developed by fine-tuning the `tiny_vit_21m_224.dist_in22k_ft_in1k` model.

The `culico-net-cls-v1` is a TinyViT image classification model. It was pretrained on the large-scale ImageNet-22k dataset using distillation and then fine-tuned on the ImageNet-1k dataset by the original paper's authors. This foundational training has been further adapted for the specific task of mosquito species classification using a dedicated dataset.

**Model Details:**

*   **Model Type:** Image classification / feature backbone
*   **Model Stats:**
    *   Parameters (M): 21.2
    *   GMACs: 4.1
    *   Activations (M): 15.9
    *   Image size: 224 x 224
*   **Papers:**
    *   TinyViT: Fast Pretraining Distillation for Small Vision Transformers: https://arxiv.org/abs/2207.10666
    *   Original GitHub Repository: https://github.com/microsoft/Cream/tree/main/TinyViT
*   **Dataset:** The model was trained on the `iloncka/mosquito-species-classification-dataset`. This is one of a suite of datasets which also includes `iloncka/mosquito-species-detection-dataset` and `iloncka/mosquito-species-segmentation-dataset`. These datasets contain images of various mosquito species, crucial for training accurate identification models. For instance, some datasets include species like *Aedes aegypti*, *Aedes albopictus*, and *Culex quinquefasciatus*, and are annotated for features like normal or smashed conditions.
*   **Pretrain Dataset:** ImageNet-22k, ImageNet-1k

**Model Usage:**

The model can be used for image classification tasks. Below is a code snippet demonstrating how to use the model with the Fastai library:

```python
from fastai.vision.all import load_learner
from PIL import Image

# It is assumed that the model has been downloaded locally
learner = load_learner(model_path)
_, _, probabilities = learner.predict(image)
```

**The CulicidaeLab Project:**

The culico-net-cls-v1 model is a component of the larger CulicidaeLab project. This project aims to provide a comprehensive suite of tools for mosquito monitoring and research. Other parts of the project include:

* **Related Models:**
  - [iloncka/culico-net-det-v1](https://huggingface.co/iloncka/culico-net-det-v1) - Detection model
  - [iloncka/culico-net-segm-v1-nano](https://huggingface.co/iloncka/culico-net-segm-v1-nano) - Segmentation model
*   **Datasets:**
    *   `iloncka/mosquito-species-detection-dataset`
    *   `iloncka/mosquito-species-segmentation-dataset`
    *   `iloncka/mosquito-species-classification-dataset`
*   **Python Library:**  https://github.com/iloncka-ds/culicidaelab
*   **Mobile Applications:**
*   - https://gitlab.com/mosquitoscan/mosquitoscan-app
    - https://github.com/iloncka-ds/culicidaelab-mobile
*   **Web Application:** https://github.com/iloncka-ds/culicidaelab-server

**Practical Applications:**

The `culico-net-cls-v1` model and the broader `CulicidaeLab` project have several practical applications:

*   **Integration into Third-Party Products:** The models can be integrated into existing applications for plant and animal identification to expand their functionality to include mosquito recognition.
*   **Embedded Systems (Edge AI):** These models can be optimized for deployment on edge devices such as smart traps, drones, or cameras for in-field monitoring without requiring a constant internet connection.
*   **Accelerating Development:** The pre-trained models can serve as a foundation for transfer learning, enabling researchers to develop systems for identifying other insects or specific mosquito subspecies more efficiently.
*   **Expert Systems:** The model can be used as a "second opinion" tool to assist specialists in quickly verifying species identification.

**Acknowledgments:**

The development of CulicidaeLab is supported by a grant from the **Foundation for Assistance to Small Innovative Enterprises ([FASIE](https://fasie.ru/))**.