metadata

license: apache-2.0
tags:
  - image-classification
  - vision
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
base_model: facebook/convnextv2-large-22k-384
model-index:
  - name: outputs
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.7777777777777778
            name: Accuracy

Cowboy Hat emoji 🤠 (Western)

This model is a fine-tuned version of facebook/convnextv2-large-22k-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.4250
Accuracy: 0.7778

Model description

When you want to know if an art is 🤠 or not 🤠.

Current iteration: v3.5 (Continuous Image Integration)

Wait, why?

gelbooru contains a lot of images, however not all of them are in the same region as south eas asia. As such, to filter out such images we have created a classifier that in theory learns the differences between western (USA/Europe/etc.) and not western (Japan/China/SEA).

The definition of "Not Western" is limited to the the asian region (Japan, Korea, China, Taiwan, Thailand and the surroundign region). The author believes that the art is similar enough with the same "style" which he personally prefers over western art.

Intended uses & limitations

filter gelbooru data on 🤠 or not 🤠

Training and evaluation data

Selected 358 images of 🤠 and not 🤠.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 802565
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7384	1.0	152	0.4268	0.7963
0.2888	2.0	304	0.4250	0.7778
0.2953	3.0	456	0.4250	0.7778
0.4914	4.0	608	0.4250	0.7778
0.4099	5.0	760	0.4250	0.7778

Framework versions

Transformers 4.30.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3