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--- |
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license: apache-2.0 |
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base_model: microsoft/resnet-152 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: Dogs-Breed-Image-Classification-V2 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8408163265306122 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Dogs-Breed-Image-Classification-V2 |
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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.). |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0115 |
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- Accuracy: 0.8408 |
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## Model description |
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[Link to the fine-tuned model using resnet-50](https://huggingface.co/jhoppanne/Dogs-Breed-Image-Classification-V0) |
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[Link to the fine-tuned model using resnet-101](https://huggingface.co/jhoppanne/Dogs-Breed-Image-Classification-V1) |
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This model was trained using dataset from [Kaggle - Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.) |
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Quotes from the website: |
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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. |
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citation: |
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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] |
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Secondary: |
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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] |
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## Intended uses & limitations |
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This model is fined tune solely for classifiying 120 species of dogs. |
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## Training and evaluation data |
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75% training data, 25% testing data. |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 483 | 4.6525 | 0.7382 | |
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| 4.7329 | 2.0 | 966 | 4.3558 | 0.7298 | |
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| 4.5033 | 3.0 | 1449 | 3.9568 | 0.7471 | |
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| 4.1405 | 4.0 | 1932 | 3.5160 | 0.7782 | |
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| 3.7176 | 5.0 | 2415 | 3.0805 | 0.7946 | |
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| 3.293 | 6.0 | 2898 | 2.6907 | 0.8021 | |
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| 2.8898 | 7.0 | 3381 | 2.3044 | 0.8126 | |
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| 2.5343 | 8.0 | 3864 | 2.0091 | 0.8177 | |
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| 2.2188 | 9.0 | 4347 | 1.7910 | 0.8126 | |
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| 1.9698 | 10.0 | 4830 | 1.6015 | 0.8194 | |
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| 1.7532 | 11.0 | 5313 | 1.4383 | 0.8220 | |
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| 1.586 | 12.0 | 5796 | 1.3355 | 0.8264 | |
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| 1.4533 | 13.0 | 6279 | 1.2467 | 0.8260 | |
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| 1.336 | 14.0 | 6762 | 1.1575 | 0.8313 | |
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| 1.2641 | 15.0 | 7245 | 1.1038 | 0.8321 | |
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| 1.185 | 16.0 | 7728 | 1.0606 | 0.8395 | |
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| 1.1329 | 17.0 | 8211 | 1.0178 | 0.8398 | |
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| 1.0977 | 18.0 | 8694 | 1.0115 | 0.8408 | |
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| 1.0732 | 19.0 | 9177 | 0.9945 | 0.8381 | |
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| 1.0508 | 20.0 | 9660 | 0.9930 | 0.8393 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.3.0 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.1 |
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