https://github.com/e0xextazy/dog_breeds_clf

Trained the ResNet50 model from scratch on the imagewoof dataset. Reached 83% accuracy
https://github.com/e0xextazy/dog_breeds_clf

classification classifier classifier-model deep-learning deep-neural-networks imagenette imagewoof resnet resnet-18 resnet-34 resnet-50 resnet50

Last synced: 3 months ago
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Trained the ResNet50 model from scratch on the imagewoof dataset. Reached 83% accuracy

• Host: GitHub
• URL: https://github.com/e0xextazy/dog_breeds_clf
• Owner: e0xextazy
• License: mit
• Created: 2022-01-29T13:21:42.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-01-29T16:19:07.000Z (over 3 years ago)
• Last Synced: 2025-03-18T19:37:47.027Z (7 months ago)
• Topics: classification, classifier, classifier-model, deep-learning, deep-neural-networks, imagenette, imagewoof, resnet, resnet-18, resnet-34, resnet-50, resnet50
• Language: Jupyter Notebook
• Homepage:
• Size: 83.7 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
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# dog_breeds_clf

### Everything is simple! Send a photo -> take a breed

## 👶 Dependencies

* [Python 3.8 or higher](https://www.python.org/downloads/)

* [Git SCM](https://git-scm.com/downloads)

* [Docker](https://docs.docker.com/get-docker/)

## 🛠️ Installation

* ### From GitHub

Step 1: 

```sh

git clone https://github.com/e0xextazy/dog_breeds_clf.git

```

Step 2: 

```sh

cd dog_breeds_clf

```

Step 3:

```sh

docker build -t  .

```

Step 4:

```sh

docker run -d -p 5000:5000 

```

* ### From DockerHub

Step 1: 

```sh

docker pull mbaushenko/dog_breeds_clf:mvp

```

Step 2:

```sh

docker run -d -p 5000:5000 mbaushenko/dog_breeds_clf:mvp

```

## 🚀 Usage

* ### From GitHub/DockerHub

```sh

python3 utils/send_data.py

```

## ⚖️ License

[MIT © 2022 Mark Baushenko](https://github.com/e0xextazy/dog_breeds_clf/blob/main/LICENSE)