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---
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tags:
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- image-classification
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- timm
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- transformers
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- fastai
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library_name: fastai
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license: apache-2.0
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datasets:
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- imagenet-1k
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- imagenet-22k
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- iloncka/mosquito-species-classification-dataset
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metrics:
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- accuracy
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base_model:
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- timm/tiny_vit_21m_224.dist_in22k_ft_in1k
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---
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# Model Card for `culico-net-cls-v1` |
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`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. |
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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. |
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**Model Details:** |
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* **Model Type:** Image classification / feature backbone |
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* **Model Stats:** |
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* Parameters (M): 21.2 |
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* GMACs: 4.1 |
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* Activations (M): 15.9 |
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* Image size: 224 x 224 |
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* **Papers:** |
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* TinyViT: Fast Pretraining Distillation for Small Vision Transformers: https://arxiv.org/abs/2207.10666 |
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* Original GitHub Repository: https://github.com/microsoft/Cream/tree/main/TinyViT |
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* **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. |
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* **Pretrain Dataset:** ImageNet-22k, ImageNet-1k |
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**Model Usage:** |
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The model can be used for image classification tasks. Below is a code snippet demonstrating how to use the model with the Fastai library: |
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```python |
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from fastai.vision.all import load_learner |
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from PIL import Image |
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# It is assumed that the model has been downloaded locally |
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learner = load_learner(model_path) |
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_, _, probabilities = learner.predict(image) |
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``` |
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**The CulicidaeLab Project:** |
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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: |
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* **Datasets:** |
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* `iloncka/mosquito-species-detection-dataset` |
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* `iloncka/mosquito-species-segmentation-dataset` |
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* `iloncka/mosquito-species-classification-dataset` |
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* **Python Library:** https://github.com/iloncka-ds/culicidaelab |
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* **Mobile Applications:** |
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* - https://gitlab.com/mosquitoscan/mosquitoscan-app |
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- https://github.com/iloncka-ds/culicidaelab-mobile |
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* **Web Application:** https://github.com/iloncka-ds/culicidaelab-server |
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**Practical Applications:** |
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The `culico-net-cls-v1` model and the broader `CulicidaeLab` project have several practical applications: |
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* **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. |
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* **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. |
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* **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. |
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* **Expert Systems:** The model can be used as a "second opinion" tool to assist specialists in quickly verifying species identification. |
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**Acknowledgments:** |
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The development of CulicidaeLab is supported by a grant from the **Foundation for Assistance to Small Innovative Enterprises ([FASIE](https://fasie.ru/))**. |
