README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: convnext-tiny-224_album_vitVMMRdb_make_model_album_pred
    results: []

convnext-tiny-224_album_vitVMMRdb_make_model_album_pred

This model is a fine-tuned version of facebook/convnext-tiny-224 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4384
• Accuracy: 0.8814
• Precision: 0.8793
• Recall: 0.8814
• F1: 0.8772

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: 64
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
4.8445	1.0	944	4.7488	0.0919	0.0214	0.0919	0.0266
3.8243	2.0	1888	3.6914	0.2379	0.1520	0.2379	0.1447
2.8783	3.0	2832	2.7011	0.4105	0.3433	0.4105	0.3235
2.1348	4.0	3776	1.9752	0.5652	0.5279	0.5652	0.5069
1.6456	5.0	4720	1.5225	0.6529	0.6274	0.6529	0.6134
1.3835	6.0	5664	1.2167	0.7106	0.6996	0.7106	0.6845
1.1258	7.0	6608	1.0067	0.7491	0.7394	0.7491	0.7272
1.0181	8.0	7552	0.8722	0.7819	0.7755	0.7819	0.7678
0.7829	9.0	8496	0.7752	0.8018	0.7987	0.8018	0.7899
0.7503	10.0	9440	0.6983	0.8202	0.8189	0.8202	0.8121
0.6534	11.0	10384	0.6392	0.8301	0.8280	0.8301	0.8220
0.6108	12.0	11328	0.5941	0.8422	0.8384	0.8422	0.8343
0.5087	13.0	12272	0.5659	0.8487	0.8462	0.8487	0.8416
0.528	14.0	13216	0.5379	0.8554	0.8536	0.8554	0.8495
0.4489	15.0	14160	0.5189	0.8589	0.8566	0.8589	0.8528
0.4252	16.0	15104	0.5072	0.8626	0.8610	0.8626	0.8579
0.4239	17.0	16048	0.4857	0.8686	0.8678	0.8686	0.8645
0.3951	18.0	16992	0.4796	0.8695	0.8675	0.8695	0.8645
0.3679	19.0	17936	0.4685	0.8739	0.8724	0.8739	0.8695
0.3694	20.0	18880	0.4604	0.8751	0.8720	0.8751	0.8697
0.3435	21.0	19824	0.4555	0.8777	0.8755	0.8777	0.8739
0.3204	22.0	20768	0.4479	0.8783	0.8763	0.8783	0.8744
0.3475	23.0	21712	0.4433	0.8794	0.8773	0.8794	0.8753
0.338	24.0	22656	0.4408	0.8809	0.8785	0.8809	0.8767
0.3437	25.0	23600	0.4384	0.8814	0.8793	0.8814	0.8772

Framework versions

• Transformers 4.24.0
• Pytorch 1.12.1+cu113
• Datasets 2.7.1
• Tokenizers 0.13.2