Image Classification
Transformers
TensorBoard
Safetensors
vit
animals
vision-transformer
transfer-learning
Generated from Trainer
Eval Results (legacy)
Instructions to use maceythm/vit-90-animals-moreepochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maceythm/vit-90-animals-moreepochs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="maceythm/vit-90-animals-moreepochs") 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("maceythm/vit-90-animals-moreepochs") model = AutoModelForImageClassification.from_pretrained("maceythm/vit-90-animals-moreepochs") - Notebooks
- Google Colab
- Kaggle
vit-90-animals-moreepochs
This model is a fine-tuned version of google/vit-base-patch16-224 on the iamsouravbanerjee/animal-image-dataset-90-different-animals dataset. It achieves the following results on the evaluation set:
- Loss: 0.0709
- Accuracy: 0.9852
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- 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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1913 | 1.0 | 270 | 0.3072 | 0.9722 |
| 0.2882 | 2.0 | 540 | 0.1545 | 0.9722 |
| 0.1824 | 3.0 | 810 | 0.1328 | 0.9704 |
| 0.1578 | 4.0 | 1080 | 0.1217 | 0.9704 |
| 0.1518 | 5.0 | 1350 | 0.1161 | 0.9704 |
| 0.1246 | 6.0 | 1620 | 0.1134 | 0.9704 |
| 0.1203 | 7.0 | 1890 | 0.1134 | 0.9704 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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Model tree for maceythm/vit-90-animals-moreepochs
Base model
google/vit-base-patch16-224Evaluation results
- Accuracy on iamsouravbanerjee/animal-image-dataset-90-different-animalsself-reported0.985