metadata

license: other
base_model: google/mobilenet_v2_1.0_224
tags:
  - generated_from_trainer
datasets:
  - food101
metrics:
  - accuracy
model-index:
  - name: mobilenet-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: 0.821

mobilenet-finetuned-food101

This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on the food101 dataset. It achieves the following results on the evaluation set:

Loss: 0.5518
Accuracy: 0.821

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: 128
eval_batch_size: 128
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 512
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
No log	1.0	6	1.9575	0.153
1.9536	2.0	12	1.8509	0.265
1.9536	3.0	18	1.7003	0.451
1.7915	4.0	24	1.5181	0.578
1.4994	5.0	30	1.3609	0.631
1.4994	6.0	36	1.2321	0.669
1.2203	7.0	42	1.0696	0.69
1.2203	8.0	48	0.9676	0.723
1.0215	9.0	54	0.8888	0.729
0.8462	10.0	60	0.8380	0.74
0.8462	11.0	66	0.7461	0.778
0.744	12.0	72	0.6724	0.792
0.744	13.0	78	0.7314	0.769
0.6496	14.0	84	0.6831	0.77
0.6143	15.0	90	0.5937	0.81
0.6143	16.0	96	0.6217	0.793
0.5468	17.0	102	0.5965	0.788
0.5468	18.0	108	0.5944	0.813
0.5428	19.0	114	0.5869	0.812
0.5193	20.0	120	0.5565	0.82
0.5193	21.0	126	0.6155	0.803
0.4902	22.0	132	0.5685	0.817
0.4902	23.0	138	0.6097	0.789
0.4869	24.0	144	0.6002	0.8
0.4745	25.0	150	0.5569	0.814
0.4745	26.0	156	0.5414	0.821
0.4653	27.0	162	0.5806	0.807
0.4653	28.0	168	0.5663	0.807
0.4543	29.0	174	0.5412	0.825
0.4575	30.0	180	0.5518	0.821

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0