---
license: apache-2.0
base_model: microsoft/beit-large-patch16-384
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: beit-large-patch16-384-limb-person-crop-8_5e-5_1e-4_0.05
  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. -->

# beit-large-patch16-384-limb-person-crop-8_5e-5_1e-4_0.05

This model is a fine-tuned version of [microsoft/beit-large-patch16-384](https://huggingface.co/microsoft/beit-large-patch16-384) on the c14kevincardenas/beta_caller_284_person_crop dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7131
- Accuracy: 0.7678

## 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: 32
- eval_batch_size: 32
- seed: 2014
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.05

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3649        | 1.0   | 214  | 1.5215          | 0.3640   |
| 1.1297        | 2.0   | 428  | 1.0014          | 0.6003   |
| 1.0881        | 3.0   | 642  | 0.9018          | 0.6559   |
| 1.0065        | 4.0   | 856  | 0.9688          | 0.5995   |
| 1.0028        | 5.0   | 1070 | 0.8240          | 0.7015   |
| 0.9225        | 6.0   | 1284 | 0.7355          | 0.7521   |
| 0.8522        | 7.0   | 1498 | 0.7693          | 0.7463   |
| 0.821         | 8.0   | 1712 | 0.7131          | 0.7678   |
| 0.735         | 9.0   | 1926 | 0.7316          | 0.7761   |
| 0.7123        | 10.0  | 2140 | 0.7301          | 0.7778   |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1