2.21 kB
| --- | |
| license: apache-2.0 | |
| tags: | |
| - image-classification | |
| - generated_from_trainer | |
| datasets: | |
| - cats_vs_dogs | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: vit-base-cats-vs-dogs | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| dataset: | |
| name: cats_vs_dogs | |
| type: cats_vs_dogs | |
| args: default | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9883257403189066 | |
| --- | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # vit-base-cats-vs-dogs | |
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cats_vs_dogs dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0369 | |
| - Accuracy: 0.9883 | |
| ## how to use | |
| ```python | |
| from transformers import ViTFeatureExtractor, ViTModel | |
| from PIL import Image | |
| import requests | |
| url = 'http://images.cocodataset.org/val2017/000000039769.jpg' | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k') | |
| model = ViTModel.from_pretrained('akahana/vit-base-cats-vs-dogs') | |
| inputs = feature_extractor(images=image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| last_hidden_states = outputs.last_hidden_state | |
| ``` | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0002 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 1337 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.0949 | 1.0 | 2488 | 0.0369 | 0.9883 | | |
| ### Framework versions | |
| - Transformers 4.12.5 | |
| - Pytorch 1.10.0+cu111 | |
| - Datasets 1.16.1 | |
| - Tokenizers 0.10.3 | |