README.md

metadata

language:
  - eng
license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - multilabel-image-classification
  - multilabel
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: resnet-50-linearhead-2024_03_12-with_data_aug_batch-size32_epochs93_freeze
    results: []

resnet-50-linearhead-2024_03_12-with_data_aug_batch-size32_epochs93_freeze

This model is a fine-tuned version of microsoft/resnet-50 on the multilabel_complete_dataset dataset. It achieves the following results on the evaluation set:

• Loss: 0.1518
• F1 Micro: 0.7545
• F1 Macro: 0.6309
• Roc Auc: 0.8276
• Accuracy: 0.4069
• Learning Rate: 1e-05

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 93

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	F1 Macro	Roc Auc	Accuracy	Rate
No log	1.0	274	0.2237	0.5839	0.2834	0.7176	0.1952	0.001
0.2683	2.0	548	0.1895	0.6773	0.4549	0.7743	0.3055	0.001
0.2683	3.0	822	0.1786	0.7021	0.5202	0.7911	0.3539	0.001
0.2058	4.0	1096	0.1715	0.7198	0.5666	0.8058	0.3667	0.001
0.2058	5.0	1370	0.1662	0.7220	0.5718	0.8050	0.3768	0.001
0.1916	6.0	1644	0.1648	0.7155	0.5721	0.7980	0.3796	0.001
0.1916	7.0	1918	0.1618	0.7281	0.5973	0.8082	0.3810	0.001
0.1858	8.0	2192	0.1598	0.7375	0.6061	0.8166	0.3855	0.001
0.1858	9.0	2466	0.1599	0.7440	0.6209	0.8223	0.3911	0.001
0.1839	10.0	2740	0.1584	0.7382	0.6047	0.8173	0.3949	0.001
0.1815	11.0	3014	0.1569	0.7414	0.6068	0.8186	0.3960	0.001
0.1815	12.0	3288	0.1585	0.7257	0.5953	0.8043	0.3963	0.001
0.1807	13.0	3562	0.1581	0.7514	0.6286	0.8311	0.3967	0.001
0.1807	14.0	3836	0.1565	0.7453	0.6230	0.8224	0.4022	0.001
0.1795	15.0	4110	0.1549	0.7504	0.6253	0.8262	0.3991	0.001
0.1795	16.0	4384	0.1573	0.7446	0.6290	0.8214	0.3939	0.001
0.178	17.0	4658	0.1551	0.7519	0.6287	0.8281	0.4026	0.001
0.178	18.0	4932	0.1570	0.7430	0.6155	0.8203	0.3914	0.001
0.1764	19.0	5206	0.1558	0.7480	0.6287	0.8236	0.3991	0.001
0.1764	20.0	5480	0.1574	0.7403	0.6085	0.8164	0.4001	0.001
0.1775	21.0	5754	0.1561	0.7532	0.6246	0.8302	0.4029	0.001
0.177	22.0	6028	0.1545	0.7596	0.6431	0.8378	0.3974	0.0001
0.177	23.0	6302	0.1556	0.7472	0.6292	0.8233	0.4026	0.0001
0.1762	24.0	6576	0.1548	0.7528	0.6343	0.8283	0.3994	0.0001
0.1762	25.0	6850	0.1554	0.7468	0.6225	0.8222	0.3994	0.0001
0.1759	26.0	7124	0.1548	0.7529	0.6326	0.8297	0.3977	0.0001
0.1759	27.0	7398	0.1552	0.7516	0.6352	0.8282	0.3970	0.0001
0.1752	28.0	7672	0.1543	0.7523	0.6328	0.8277	0.4092	0.0001
0.1752	29.0	7946	0.1545	0.7506	0.6312	0.8265	0.4019	0.0001
0.1757	30.0	8220	0.1550	0.7554	0.6394	0.8340	0.4040	0.0001
0.1757	31.0	8494	0.1554	0.7512	0.6345	0.8279	0.4022	0.0001
0.1758	32.0	8768	0.1545	0.7513	0.6302	0.8275	0.4033	0.0001
0.1755	33.0	9042	0.1555	0.7456	0.6261	0.8223	0.3977	0.0001
0.1755	34.0	9316	0.1533	0.7515	0.6307	0.8260	0.4109	0.0001
0.1752	35.0	9590	0.1551	0.7506	0.6325	0.8261	0.4054	0.0001
0.1752	36.0	9864	0.1530	0.7539	0.6299	0.8287	0.4026	0.0001
0.1752	37.0	10138	0.1546	0.7464	0.6270	0.8223	0.4036	0.0001
0.1752	38.0	10412	0.1549	0.7539	0.6364	0.8314	0.3987	0.0001
0.1763	39.0	10686	0.1547	0.7579	0.6421	0.8361	0.3977	0.0001
0.1763	40.0	10960	0.1544	0.7539	0.6345	0.8302	0.4005	0.0001
0.176	41.0	11234	0.1557	0.7536	0.6347	0.8298	0.4015	0.0001
0.1758	42.0	11508	0.1540	0.7474	0.6277	0.8226	0.3960	0.0001
0.1758	43.0	11782	0.1548	0.7578	0.6384	0.8374	0.3970	1e-05
0.1764	44.0	12056	0.1543	0.7582	0.6398	0.8352	0.4012	1e-05
0.1764	45.0	12330	0.1544	0.7448	0.6206	0.8196	0.3991	1e-05
0.1746	46.0	12604	0.1546	0.7452	0.6223	0.8208	0.4050	1e-05

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0+cu118
• Datasets 2.14.5
• Tokenizers 0.15.0