pytorch tensorboard vit image-classification transformers huggingpics model-index
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy

model-index:
- name: dog-races-v2
  results:
  - task:
      name: Image Classification
      type: image-classification
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8299999833106995
---

# dog-races-v2


Autogenerated Model created thannks to HuggingPics🤗🖼️. You can create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).

This Model is an improvement to my last model, where the Chow Chow data included images of American pickles with the same name (contaminated data). 
Current labels are: 1) Border Collie, 2) Chow Chow, 3) German Shepherd, 4) Golden Retriever, 5) Pug, 6) Rottweiler, 7) Shiba Inu, 8) Siberian Husky and 9) Tibetan Mastiff. 

There is still room for improvement.

Model Accuracy: 82.99%

When tested with Stanford Dogs Dataset, these were the results:

- Golden Retriever: 90% (117/130 images labeled correctly)
- Chow Chow: 97.45% (191/196 images labeled correctly)
- Tibetan Mastiff: 12.5% (19/152 images labeled correctly). Probably some issue with the data (most were labeled as Chow Chow).

## Example Images


#### Border Collie

![Border Collie](images/Border_Collie.jpg)

#### Chow Chow dog

![Chow Chow dog](images/Chow_Chow_dog.jpg)

#### German Shepherd

![German Shepherd](images/German_Shepherd.jpg)

#### Golden Retriever

![Golden Retriever](images/Golden_Retriever.jpg)

#### Pug

![Pug](images/Pug.jpg)

#### Rottweiler

![Rottweiler](images/Rottweiler.jpg)

#### Shiba Inu

![Shiba Inu](images/Shiba_Inu.jpg)

#### Siberian Husky

![Siberian Husky](images/Siberian_Husky.jpg)

#### Tibetan Mastiff

![Tibetan Mastiff](images/Tibetan_Mastiff.jpg)