Image Classification
Transformers
TensorBoard
Safetensors
vit
SGH
3_class
ViT
Generated from Trainer
Instructions to use Uniteworker/ViT_dog_food with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Uniteworker/ViT_dog_food with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Uniteworker/ViT_dog_food") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Uniteworker/ViT_dog_food") model = AutoModelForImageClassification.from_pretrained("Uniteworker/ViT_dog_food", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Uniteworker/ViT_dog_food: direct link, hf CLI and curl.
- Browser
- Download file 1.56 kB
-
https://huggingface.co/Uniteworker/ViT_dog_food/resolve/main/README.md
- Command line
-
hf download hf://Uniteworker/ViT_dog_food/README.md
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curl -L -o README.md https://huggingface.co/Uniteworker/ViT_dog_food/resolve/main/README.md
1.56 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google/vit-base-patch16-224-in21k | |
| tags: | |
| - SGH | |
| - 3_class | |
| - ViT | |
| - generated_from_trainer | |
| model-index: | |
| - name: ViT_dog_food | |
| results: [] | |
| <!-- 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_dog_food | |
| 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 dog_foods dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4279 | |
| ## 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: 2e-05 | |
| - train_batch_size: 64 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 36 | 0.4279 | | |
| | No log | 2.0 | 72 | 0.2084 | | |
| | No log | 3.0 | 108 | 0.1726 | | |
| ### Framework versions | |
| - Transformers 4.50.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.5.0 | |
| - Tokenizers 0.21.1 | |