metadata

license: apache-2.0
tags:
  - image-classification
  - vision
  - generated_from_trainer
datasets:
  - AI-Lab-Makerere/beans
metrics:
  - accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: vit-base-beans
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: beans
          type: beans
          config: default
          split: validation
          args: default
        metrics:
          - type: accuracy
            value: 0.9849624060150376
            name: Accuracy

THIS IS A TEST REPO FOR DEBUGGING!

This repo is here as a result of playing with and debugging training scripts and push to hub features. As such, the TesnorFlow and PyTorch models will be out of sync and different weights may be push at any time, including pushing models with very low performance.

vit-base-beans

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset. It achieves the following results on the evaluation set:

Loss: 0.0630
Accuracy: 0.9850

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 1337
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
0.3038	1.0	130	0.2396	0.9624
0.1609	2.0	260	0.1130	0.9774
0.2313	3.0	390	0.0809	0.9850
0.1436	4.0	520	0.0738	0.9850
0.1086	5.0	650	0.0630	0.9850

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.14.0.dev20221118
Datasets 2.9.1.dev0
Tokenizers 0.13.2