Update README.md

README.md

@@ -1,33 +1,77 @@
----
-license: apache-2.0
-tags:
-- fastai
----
+---
+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
+---
 
-# Amazing!
+# Model Card for `culico-net-cls-v1`
 
-🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
+`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.
 
-# Some next steps
-1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
+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.
 
-2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
+**Model Details:**
 
-3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
+*   **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
 
-Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
+**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)
+```
 
-# Model card
+**The CulicidaeLab Project:**
 
-## Model description
-More information needed
+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:
 
-## Intended uses & limitations
-More information needed
+*   **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
 
-## Training and evaluation data
-More information needed
+**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/))**.