README.md

metadata

library_name: transformers
base_model: google/mobilenet_v2_1.0_224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: mobilenet_v2_1.0_224-finetuned-papsmear
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8676470588235294

mobilenet_v2_1.0_224-finetuned-papsmear

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

• Loss: 0.4698
• Accuracy: 0.8676

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7932	0.9935	38	1.7607	0.25
1.6542	1.9869	76	1.5736	0.3971
1.4692	2.9804	114	1.4805	0.3676
1.2759	4.0	153	1.2177	0.5809
1.1521	4.9935	191	1.0727	0.6471
1.078	5.9869	229	0.9996	0.6176
1.0235	6.9804	267	0.8680	0.7059
0.9554	8.0	306	0.9273	0.6397
0.7437	8.9935	344	0.7389	0.7059
0.7876	9.9869	382	0.6774	0.7426
0.7698	10.9804	420	0.6569	0.7206
0.7597	12.0	459	0.6758	0.7574
0.6114	12.9935	497	0.8279	0.7132
0.6847	13.9869	535	0.7505	0.7132
0.5902	14.9804	573	0.7919	0.6691
0.629	16.0	612	0.6117	0.7868
0.5071	16.9935	650	0.6048	0.7353
0.5453	17.9869	688	0.8086	0.7279
0.5071	18.9804	726	0.7835	0.7059
0.5328	20.0	765	0.6139	0.75
0.5053	20.9935	803	0.5981	0.7868
0.4436	21.9869	841	0.5219	0.8015
0.5025	22.9804	879	0.4959	0.8088
0.4984	24.0	918	0.5701	0.7794
0.4655	24.9935	956	0.7179	0.7206
0.3848	25.9869	994	0.5075	0.8088
0.3824	26.9804	1032	0.6645	0.7426
0.4901	28.0	1071	0.7288	0.6985
0.397	28.9935	1109	0.7251	0.7279
0.3818	29.9869	1147	0.6250	0.7941
0.3412	30.9804	1185	0.7065	0.7279
0.3627	32.0	1224	0.6877	0.7426
0.3557	32.9935	1262	0.4245	0.8529
0.441	33.9869	1300	0.6974	0.75
0.3036	34.9804	1338	0.6458	0.7426
0.3213	36.0	1377	0.5579	0.7941
0.402	36.9935	1415	0.4578	0.8382
0.2897	37.9869	1453	0.5369	0.7868
0.348	38.9804	1491	0.6819	0.7941
0.3929	40.0	1530	0.5810	0.7868
0.3173	40.9935	1568	0.7875	0.7426
0.3499	41.9869	1606	0.5051	0.8015
0.3053	42.9804	1644	0.7510	0.7426
0.4109	44.0	1683	0.6529	0.75
0.3846	44.9935	1721	0.9615	0.7132
0.3222	45.9869	1759	0.8889	0.6691
0.3293	46.9804	1797	0.4698	0.8676
0.293	48.0	1836	0.5996	0.8015
0.2363	48.9935	1874	0.5007	0.8309
0.2811	49.9869	1912	0.6748	0.7941
0.2403	50.9804	1950	0.6595	0.7941
0.2553	52.0	1989	0.5987	0.7794
0.2959	52.9935	2027	0.5459	0.8235
0.3066	53.9869	2065	0.6198	0.7868
0.2981	54.9804	2103	0.4886	0.8309
0.2658	56.0	2142	0.6422	0.7794
0.2371	56.9935	2180	0.5000	0.8382
0.2331	57.9869	2218	0.8854	0.7132
0.2777	58.9804	2256	0.6190	0.8015
0.3047	59.6078	2280	0.6048	0.7647

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1