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# Dogs-Breed-Image-Classification-V2

This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the [Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.).
It achieves the following results on the evaluation set:
- Loss: 1.0115
- Accuracy: 0.8408

## Model description

- [Link to the fine-tuned model using resnet-50](https://huggingface.co/jhoppanne/Dogs-Breed-Image-Classification-V0)
- [Link to the fine-tuned model using resnet-101](https://huggingface.co/jhoppanne/Dogs-Breed-Image-Classification-V1)

This model was trained using dataset from [Kaggle - Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.)

Quotes from the website:
The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age.

citation:
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex]

Secondary:
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex]
## Intended uses & limitations

This model is fined tune solely for classifiying 120 species of dogs.

## Training and evaluation data

75% training data, 25% testing data.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 483  | 4.6525          | 0.7382   |
| 4.7329        | 2.0   | 966  | 4.3558          | 0.7298   |
| 4.5033        | 3.0   | 1449 | 3.9568          | 0.7471   |
| 4.1405        | 4.0   | 1932 | 3.5160          | 0.7782   |
| 3.7176        | 5.0   | 2415 | 3.0805          | 0.7946   |
| 3.293         | 6.0   | 2898 | 2.6907          | 0.8021   |
| 2.8898        | 7.0   | 3381 | 2.3044          | 0.8126   |
| 2.5343        | 8.0   | 3864 | 2.0091          | 0.8177   |
| 2.2188        | 9.0   | 4347 | 1.7910          | 0.8126   |
| 1.9698        | 10.0  | 4830 | 1.6015          | 0.8194   |
| 1.7532        | 11.0  | 5313 | 1.4383          | 0.8220   |
| 1.586         | 12.0  | 5796 | 1.3355          | 0.8264   |
| 1.4533        | 13.0  | 6279 | 1.2467          | 0.8260   |
| 1.336         | 14.0  | 6762 | 1.1575          | 0.8313   |
| 1.2641        | 15.0  | 7245 | 1.1038          | 0.8321   |
| 1.185         | 16.0  | 7728 | 1.0606          | 0.8395   |
| 1.1329        | 17.0  | 8211 | 1.0178          | 0.8398   |
| 1.0977        | 18.0  | 8694 | 1.0115          | 0.8408   |
| 1.0732        | 19.0  | 9177 | 0.9945          | 0.8381   |
| 1.0508        | 20.0  | 9660 | 0.9930          | 0.8393   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.15.0
- Tokenizers 0.15.1

afghan_hound african_hunting_dog border_terrier kuvasz malamute malinois miniature_pinscher miniature_poodle miniature_schnauzer otterhound papillon pug redbone boston_bull schipperke silky_terrier soft standard_poodle standard_schnauzer toy_poodle toy_terrier vizsla whippet wire bouvier_des_flandres brabancon_griffon brittany_spaniel cardigan chesapeake_bay_retriever chihuahua dandie_dinmont doberman airedale english_foxhound english_setter english_springer entlebucher eskimo_dog french_bulldog german_shepherd german_short gordon_setter great_dane american_staffordshire_terrier great_pyrenees greater_swiss_mountain_dog ibizan_hound irish_setter irish_terrier irish_water_spaniel irish_wolfhound italian_greyhound japanese_spaniel kerry_blue_terrier appenzeller labrador_retriever lakeland_terrier leonberg lhasa maltese_dog mexican_hairless newfoundland norfolk_terrier norwegian_elkhound norwich_terrier australian_terrier old_english_sheepdog pekinese pembroke pomeranian rhodesian_ridgeback rottweiler saint_bernard saluki samoyed scotch_terrier bedlington_terrier scottish_deerhound sealyham_terrier shetland_sheepdog shih siberian_husky staffordshire_bullterrier sussex_spaniel tibetan_mastiff tibetan_terrier walker_hound bernese_mountain_dog weimaraner welsh_springer_spaniel west_highland_white_terrier yorkshire_terrier affenpinscher basenji basset beagle black bloodhound blenheim_spaniel bluetick borzoi boxer briard bull_mastiff cairn chow clumber cocker_spaniel collie border_collie curly dhole dingo flat giant_schnauzer golden_retriever groenendael keeshond kelpie komondor