File size: 1,825 Bytes
931fc57 824971c 931fc57 bde48d0 931fc57 824971c 931fc57 bde48d0 2c0ed02 100bc08 b859d55 2c0ed02 931fc57 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
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
#### Chow Chow dog
#### German Shepherd
#### Golden Retriever
#### Pug
#### Rottweiler
#### Shiba Inu
#### Siberian Husky
#### Tibetan Mastiff
 |