---
language: en
license: mit 
library_name: peft
tags:
- image-classification
- pytorch
- resnet
- lora
- birds
- cub-200-2011
- fine-tuning
- computer-vision
datasets:
- cub-200-2011 # 使用 Hugging Face Datasets 库的标识符（如果存在）或自定义名称
pipeline_tag: image-classification
widget:
- src: https://images.unsplash.com/photo-1518992028580-6d57bd80f2dd?ixlib=rb-1.2.1&auto=format&fit=crop&w=600&q=80 # 示例图片 URL
  example_title: Example Bird 1 (e.g., Cardinal)
- src: https://images.unsplash.com/photo-1552728089-57bdde30beb3?ixlib=rb-1.2.1&auto=format&fit=crop&w=600&q=80 # 示例图片 URL 2
  example_title: Example Bird 2 (e.g., Blue Jay)
---

# ResNet50 + LoRA for Bird Classification (CUB-200-2011)

This repository contains LoRA (Low-Rank Adaptation) adapters fine-tuned on the CUB-200-2011 dataset for bird image classification. These adapters are designed to be applied to a standard `torchvision.models.resnet50` base model.

## Model Details

*   **Base Model:** `torchvision.models.resnet50` (pre-trained on ImageNet).
*   **Fine-tuning Method:** Low-Rank Adaptation (LoRA) using the `peft` library.
*   **Dataset:** [Caltech-UCSD Birds-200-2011 (CUB-200-2011)](https://data.caltech.edu/records/65de6-vp158)
*   **Number of Classes:** 200 bird species.
*   **LoRA Configuration:**
    *   Rank (`r`): 8 (as used in training, please verify/update)
    *   Alpha (`lora_alpha`): 16 (as used in training, please verify/update)
    *   Target Modules: ["fc", "conv1", "layer4.0.conv1"] (Please list the actual modules targeted during training)
    *   Dropout: 0.05
    *   Bias: "none"

## How to Use

First, make sure you have `torch`, `torchvision`, and `peft` installed:
```bash
pip install torch torchvision peft Pillow