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library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- animals
- vision-transformer
- vit
- transfer-learning
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-90-animals-moreepochs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: iamsouravbanerjee/animal-image-dataset-90-different-animals
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9851851851851852
---
<!-- 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-90-animals-moreepochs
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/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
|