metadata

license: other
base_model: apple/mobilevitv2-1.0-imagenet1k-256
tags:
  - generated_from_trainer
datasets:
  - food101
metrics:
  - accuracy
model-index:
  - name: mobilevit-finetuned-food101
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: food101
          type: food101
          config: default
          split: train[:5000]
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.874

mobilevit-finetuned-food101

This model is a fine-tuned version of apple/mobilevitv2-1.0-imagenet1k-256 on the food101 dataset. It achieves the following results on the evaluation set:

Loss: 0.4191
Accuracy: 0.874

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9487	0.98	23	1.9476	0.151
1.9273	2.0	47	1.9070	0.24
1.8561	2.98	70	1.8401	0.448
1.7788	4.0	94	1.7301	0.612
1.6586	4.98	117	1.5863	0.676
1.4603	6.0	141	1.4199	0.72
1.3027	6.98	164	1.2215	0.734
1.1717	8.0	188	1.0581	0.759
0.9601	8.98	211	0.9013	0.769
0.8482	10.0	235	0.7866	0.791
0.7276	10.98	258	0.7112	0.803
0.6449	12.0	282	0.6132	0.835
0.6279	12.98	305	0.6069	0.83
0.5982	14.0	329	0.5637	0.832
0.5766	14.98	352	0.5149	0.857
0.5345	16.0	376	0.5392	0.837
0.494	16.98	399	0.5017	0.848
0.4953	18.0	423	0.5002	0.846
0.5118	18.98	446	0.4782	0.856
0.4708	20.0	470	0.4898	0.858
0.4774	20.98	493	0.4769	0.851
0.4848	22.0	517	0.4665	0.841
0.4533	22.98	540	0.4890	0.837
0.4449	24.0	564	0.4558	0.857
0.4205	24.98	587	0.4767	0.857
0.4417	26.0	611	0.4476	0.853
0.4333	26.98	634	0.4853	0.834
0.4545	28.0	658	0.4573	0.847
0.4489	28.98	681	0.4659	0.845
0.4172	29.36	690	0.4191	0.874

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0