Instructions to use print1Username/COS30082-Individual-Assignment.model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use print1Username/COS30082-Individual-Assignment.model with timm:
import timm model = timm.create_model("hf-hub:print1Username/COS30082-Individual-Assignment.model", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Delete README.md
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README.md
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---
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datasets:
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- print1Username/COS30082-Assignment-1
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language:
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- en
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- zh
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base_model:
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- microsoft/resnet-50
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pipeline_tag: image-classification
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tags:
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- image-classification
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- pytorch
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metrics:
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- accuracy
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library_name: timm
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---
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<div align="center">
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[!\[English\]\(https://img.shields.io/badge/Language-English-blue?style=for-the-badge)](README.md)
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[!\[简体中文\]\(https://img.shields.io/badge/Language-%E7%AE%80%E4%BD%93%E4%B8%AD%E6%96%87-red?style=for-the-badge)](README.zh-CN.md)
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</div>
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# COS30082 Assignment 1 - Bird Species Classification
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## Model Description
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This is an image classification model trained to identify 200 different bird species from the Caltech-UCSD Birds 200 (CUB-200) dataset. This model was developed as part of the COS30082 Assignment 1.
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## Intended Use & Limitations
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- **Intended Use**: Academic purpose for predicting bird species based on images.
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- **Limitations**: The model is specifically trained on North American bird species included in the CUB-200 dataset. It may not perform well on bird species outside this dataset or in significantly different environments/lighting conditions.
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## Training Data
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The model was trained using the **Caltech-UCSD Birds 200 (CUB-200)** dataset, which contains photos of 200 bird species.
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- Dataset source: http://www.vision.caltech.edu/visipedia/CUB-200.html
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## Evaluation Results
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Based on the testing dataset provided, the model achieves the following performance metrics as required by the assignment guidelines:
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- **Top-1 accuracy**: 78.5%
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- **Average accuracy per class**: 77.2%
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## How to Use
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Here is how to load and use the model in PyTorch:
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```python
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import torch
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from huggingface_hub import hf_hub_download
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# Define your model architecture first
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# model = MyBirdClassifier()
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# Download and load the weights
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model_path = hf_hub_download(repo_id="print1Username/COS30082-Assignment-1-model", filename="bird_model.pth")
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model.load_state_dict(torch.load(model_path, map_location="cpu"))
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model.eval()
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```
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<div align="center">
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语言切换: [English](README.md) · [简体中文](README.zh-CN.md)
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</div>
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