# Environmental Monitoring

A multi-label image classification model fine-tuned based on SigLIP architecture, capable of identifying 7 types of geographic scene elements

This model is a fine-tuned object detection model based on microsoft/conditional-detr-resnet-50, specifically designed for trash detection tasks, with data augmentation techniques applied during training.

This model is a vision model based on the Vision Transformer (ViT) architecture, focusing on the classification task of algae images.

This model is a fine-tuned version based on the Vision Transformer architecture, primarily used for algae image classification tasks, achieving an accuracy of 62.09% on the evaluation dataset.

A fine-tuned model based on Google's Vision Transformer (ViT) architecture, specifically designed for algae image classification tasks

This model is a vision classification model fine-tuned on an algae dataset based on Google's ViT model, primarily used for algae image classification tasks.

An image segmentation model fine-tuned based on nvidia/mit-b5 for garbage recognition tasks

An image classification model based on YOLOv8 for identifying fog and smoke scenes.

A YOLOv5m-based object detection model, specifically designed for garbage detection tasks.

A garbage object detection model based on YOLOv5n for identifying and classifying waste items.

An image classification model built with PyTorch and HuggingPics, specifically designed to identify different types of pollution