Instructions to use Tianmu28/city_multiclass_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tianmu28/city_multiclass_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Tianmu28/city_multiclass_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Tianmu28/city_multiclass_classification") model = AutoModelForImageClassification.from_pretrained("Tianmu28/city_multiclass_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
city_multiclass_classification
This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2000
- Accuracy: 0.9667
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use 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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.6756 | 1.0 | 53 | 1.2548 | 0.8389 |
| 0.3699 | 2.0 | 106 | 0.3864 | 0.9667 |
| 0.0426 | 3.0 | 159 | 0.1737 | 0.9889 |
| 0.0101 | 4.0 | 212 | 0.1243 | 0.9889 |
| 0.0062 | 5.0 | 265 | 0.1115 | 0.9889 |
| 0.0046 | 6.0 | 318 | 0.1028 | 0.9889 |
| 0.0037 | 7.0 | 371 | 0.0979 | 0.9889 |
| 0.0034 | 8.0 | 424 | 0.0928 | 0.9889 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for Tianmu28/city_multiclass_classification
Base model
google/vit-base-patch16-224