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---
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:
* **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/))**. |