Update README.md

README.md

@@ -1,17 +1,44 @@
 ---
-license: apache-2.0
-pipeline_tag: mask-generation
+tags:
+  - mask-generation
+  - sam2
+  - segmentation
+  - instance-segmentation
+  - video-segmentation
 library_name: sam2
+pipeline_tag: mask-generation
+license: apache-2.0
+datasets:
+  - iloncka/mosquito-species-segmentation-dataset
+base_model:
+  - facebook/sam2-hiera-tiny
 ---
-Model cloned from this [repo](https://github.com/facebookresearch/segment-anything-2/) and will be finetuned.
-Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the [SAM 2 paper](https://arxiv.org/abs/2408.00714) for more information.
+Model Card for culico-net-segm-v1-nano
+
+culico-net-segm-v1-nano - instance segmentation model focused on segmenting mosquito specimens in images and videos. This model is a result of the CulicidaeLab project and was developed by fine-tuning the SAM2 (Segment Anything Model 2) architecture.
+
+The culico-net-segm-v1-nano is based on SAM2's efficient Hiera-Tiny backbone and was specifically adapted for mosquito specimen segmentation using a dedicated dataset. This model can handle both image segmentation and video segmentation tasks with prompt-based interaction.
 
-The official code is publicly release in this [repo](https://github.com/facebookresearch/segment-anything-2/).
+**Model Details:**
+- **Model Type:** Instance segmentation / Mask generation
+- **Architecture:** SAM2 with Hiera-Tiny backbone
+- **Input:** Images or video frames with optional prompts (points, boxes)
+- **Output:** Binary masks for mosquito specimens
+- **Backbone:** Hiera-Tiny (efficient transformer architecture)
 
-## Usage
+**Papers:**
+- SAM 2: Segment Anything in Images and Videos: https://arxiv.org/abs/2408.00714
+- Original GitHub Repository: https://github.com/facebookresearch/sam2
 
-For image prediction:
+**Dataset:** The model was trained on the iloncka/mosquito-species-segmentation-dataset. This is one of a suite of datasets which also includes iloncka/mosquito-species-detection-dataset and iloncka/mosquito-species-classification-dataset. These datasets contain images of various mosquito species with detailed segmentation annotations, crucial for training accurate segmentation models.
 
+**Pretrain Dataset:** ImageNet-1k, SA-1B (Segment Anything 1B dataset)
+
+**Model Usage:**
+
+The model can be used for both image and video segmentation tasks. Below are code snippets demonstrating how to use the model:
+
+**For image prediction:**
 ```python
 import torch
 from sam2.sam2_image_predictor import SAM2ImagePredictor
@@ -23,8 +50,7 @@ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
     masks, _, _ = predictor.predict(<input_prompts>)
 ```
 
-For video prediction:
-
+**For video prediction:**
 ```python
 import torch
 from sam2.sam2_video_predictor import SAM2VideoPredictor
@@ -35,19 +61,55 @@ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
     state = predictor.init_state(<your_video>)
 
     # add new prompts and instantly get the output on the same frame
-    frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):
+    frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>)
 
     # propagate the prompts to get masklets throughout the video
     for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
         ...
 ```
 
-Refer to the [demo notebooks](https://github.com/facebookresearch/segment-anything-2/tree/main/notebooks) for details.
+Refer to the [official SAM2 demo notebooks](https://github.com/facebookresearch/sam2) for details.
 
-### Citation
+**The CulicidaeLab Project:**
 
-To cite the paper, model, or software, please use the below:
-```
+The culico-net-segm-v1-nano 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-cls-v1](https://huggingface.co/iloncka/culico-net-cls-v1) - Classification model
+  - [iloncka/culico-net-det-v1](https://huggingface.co/iloncka/culico-net-det-v1)
+
+- **Datasets:**
+  - [iloncka/mosquito-species-detection-dataset](https://huggingface.co/datasets/iloncka/mosquito-species-detection-dataset)
+  - [iloncka/mosquito-species-segmentation-dataset](https://huggingface.co/datasets/iloncka/mosquito-species-segmentation-dataset)
+  - [iloncka/mosquito-species-classification-dataset](https://huggingface.co/datasets/iloncka/mosquito-species-classification-dataset)
+
+- **Python Library:** [https://github.com/iloncka-ds/culicidaelab](https://github.com/iloncka-ds/culicidaelab)
+
+- **Mobile Applications:**
+  - [https://gitlab.com/mosquitoscan/mosquitoscan-app](https://gitlab.com/mosquitoscan/mosquitoscan-app)
+  - [https://github.com/iloncka-ds/culicidaelab-mobile](https://github.com/iloncka-ds/culicidaelab-mobile)
+
+- **Web Application:** [https://github.com/iloncka-ds/culicidaelab-server](https://github.com/iloncka-ds/culicidaelab-server)
+
+**Practical Applications:**
+
+The culico-net-segm-v1-nano model and the broader CulicidaeLab project have several practical applications:
+
+- **Precise Morphological Analysis:** Enables detailed examination of mosquito anatomical features for species identification and research.
+- **Automated Specimen Measurement:** Can be used to automatically measure wing dimensions, body size, and other morphological characteristics.
+- **Training Data Generation:** Creates high-quality segmentation masks for training other computer vision models.
+- **Embedded Systems (Edge AI):** The efficient architecture allows deployment on edge devices for real-time mosquito monitoring in field conditions.
+- **Expert Systems Integration:** Provides precise segmentation capabilities that can assist entomologists in detailed specimen analysis.
+
+**Acknowledgments:**
+
+The development of CulicidaeLab is supported by a grant from the Foundation for Assistance to Small Innovative Enterprises (FASIE).
+
+**Citation:**
+
+To cite the SAM2 paper, model, or software, please use the below:
+
+```bibtex
 @article{ravi2024sam2,
   title={SAM 2: Segment Anything in Images and Videos},
   author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},