metadata

license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: |-
      DeiT-base-DatasetDict({
          train: Dataset({
              features: ['img', 'fine_label', 'coarse_label'],
              num_rows: 50000
          })
          test: Dataset({
              features: ['img', 'fine_label', 'coarse_label'],
              num_rows: 10000
          })
          validation: Dataset({
              features: ['img', 'fine_label', 'coarse_label'],
              num_rows: 10000
          })
      })
    results: []

DeiT-base-DatasetDict({

train: Dataset({
    features: ['img', 'fine_label', 'coarse_label'],
    num_rows: 50000
})
test: Dataset({
    features: ['img', 'fine_label', 'coarse_label'],
    num_rows: 10000
})
validation: Dataset({
    features: ['img', 'fine_label', 'coarse_label'],
    num_rows: 10000
})

})

This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the cifar100 dataset. It achieves the following results on the evaluation set:

Loss: 0.3054
Accuracy: 0.906

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: 64
eval_batch_size: 1
seed: 777
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
1.1232	1.0	782	0.8416	0.5390
0.9017	2.0	1564	0.8699	0.4365
0.7565	3.0	2346	0.8858	0.3678
0.706	4.0	3128	0.8952	0.3446
0.6353	5.0	3910	0.8986	0.3331
0.5384	6.0	4692	0.9001	0.3223
0.5004	7.0	5474	0.9018	0.3249
0.4672	8.0	6256	0.904	0.3113
0.4526	9.0	7038	0.9054	0.3081
0.4289	10.0	7820	0.906	0.3054

Framework versions

Transformers 4.38.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2