---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- bird species identification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification_obipix_birdID
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: private crawled images
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9719696025912545
---

<!-- 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. -->

# image_classification_obipix_birdID

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the private crawled images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1150
- Accuracy: 0.9720

## 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: 0.0002
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 6.9257        | 0.18  | 1000  | 5.3830          | 0.1638   |
| 3.9727        | 0.35  | 2000  | 2.7695          | 0.4797   |
| 2.057         | 0.53  | 3000  | 1.5070          | 0.6936   |
| 1.2103        | 0.7   | 4000  | 0.9727          | 0.7842   |
| 0.8513        | 0.88  | 5000  | 0.7101          | 0.8318   |
| 0.5836        | 1.06  | 6000  | 0.5797          | 0.8561   |
| 0.3545        | 1.23  | 7000  | 0.5066          | 0.8730   |
| 0.314         | 1.41  | 8000  | 0.4521          | 0.8818   |
| 0.2858        | 1.58  | 9000  | 0.3915          | 0.8960   |
| 0.2482        | 1.76  | 10000 | 0.3564          | 0.9056   |
| 0.2192        | 1.93  | 11000 | 0.3131          | 0.9148   |
| 0.1271        | 2.11  | 12000 | 0.2916          | 0.9207   |
| 0.0779        | 2.29  | 13000 | 0.2727          | 0.9260   |
| 0.0749        | 2.46  | 14000 | 0.2597          | 0.9309   |
| 0.0682        | 2.64  | 15000 | 0.2415          | 0.9355   |
| 0.0615        | 2.81  | 16000 | 0.2268          | 0.9385   |
| 0.0566        | 2.99  | 17000 | 0.2084          | 0.9440   |
| 0.0197        | 3.17  | 18000 | 0.1951          | 0.9475   |
| 0.0158        | 3.34  | 19000 | 0.1843          | 0.9513   |
| 0.0145        | 3.52  | 20000 | 0.1746          | 0.9541   |
| 0.0118        | 3.69  | 21000 | 0.1649          | 0.9573   |
| 0.0103        | 3.87  | 22000 | 0.1531          | 0.9599   |
| 0.006         | 4.05  | 23000 | 0.1379          | 0.9644   |
| 0.0016        | 4.22  | 24000 | 0.1316          | 0.9668   |
| 0.0013        | 4.4   | 25000 | 0.1265          | 0.9686   |
| 0.0014        | 4.57  | 26000 | 0.1232          | 0.9697   |
| 0.0009        | 4.75  | 27000 | 0.1189          | 0.9712   |
| 0.001         | 4.92  | 28000 | 0.1150          | 0.9720   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0