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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-ches-demo-v0
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9870689655172413
widget:
- src: https://imgs.mongabay.com/wp-content/uploads/sites/20/2020/04/07204605/amazon_coca_01.jpg
example_title: Tree Canopy
- src: https://images.ctfassets.net/nzn0tepgtyr1/4tyavnFHhmNuVky1ISq51k/64aaf596f6b8ee12d0f0e898679c8f4f/Hero_Image.jpg?w=1024&h=710&fl=progressive&q=50&fm=jpg&bg=transparent
example_title: Low Vegetation
- src: https://outline-prod.imgix.net/20170228-YxGtsv8J0ePP0rXcnle2?auto=format&q=60&w=1280&s=27916f48ed9226c2a2b7848de8d7c0d1
example_title: Impervious Surfaces
- src: https://clarity.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/MapServer/tile/15/11883/10109
example_title: Water
---
<!-- 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-base-ches-demo-v0
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0420
- Accuracy: 0.9871
## 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: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0183 | 3.45 | 300 | 0.0420 | 0.9871 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|