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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- AI-Lab-Makerere/beans
metrics:
- accuracy
base_model: microsoft/beit-base-patch16-384
model-index:
- name: beit_large512_fine_tuned
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: beans
type: beans
config: train
split: validation
args: train
metrics:
- type: accuracy
value: 0.9924812030075187
name: Accuracy
---
<!-- 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. -->
# beit_large512_fine_tuned
This model is a fine-tuned version of [microsoft/beit-base-patch16-384](https://huggingface.co/microsoft/beit-base-patch16-384) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0353
- Accuracy: 0.9925
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.6571 | 0.98 | 16 | 0.3870 | 0.8722 |
| 0.2299 | 1.97 | 32 | 0.0632 | 0.9850 |
| 0.1435 | 2.95 | 48 | 0.0353 | 0.9925 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3
|