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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: vogue-fashion-collection-15-nobg |
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results: [] |
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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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# vogue-fashion-collection-15-nobg |
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## Model description |
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This model classifies an image into a fashion collection. It is trained on the [tonyassi/vogue-runway-top15-512px-nobg](https://huggingface.co/datasets/tonyassi/vogue-runway-top15-512px-nobg) dataset and fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). |
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Because the model trained on a dataset with white background it is suggested to only give the model an image with a white background. Removing the background allows the model to focus on the clothes and disregard the background. |
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## Dataset description |
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[tonyassi/vogue-runway-top15-512px-nobg](https://huggingface.co/datasets/tonyassi/vogue-runway-top15-512px-nobg) |
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- 15 fashion houses |
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- 1679 collections |
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- 87,547 images |
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- No background |
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### How to use |
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```python |
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from transformers import pipeline |
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# Initialize image classification pipeline |
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pipe = pipeline("image-classification", model="tonyassi/vogue-fashion-collection-15-nobg") |
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# Perform classification |
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result = pipe('image.png') |
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# Print results |
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print(result) |
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``` |
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## Training and evaluation data |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5880 |
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- Accuracy: 0.8403 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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