metadata

license: other
base_model: nvidia/mit-b1
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: segformer-b1-finetuned-segments-graffiti
    results: []

segformer-b1-finetuned-segments-graffiti

This model is a fine-tuned version of nvidia/mit-b1 on the Adriatogi/graffiti dataset. It achieves the following results on the evaluation set:

Loss: 0.2171
Mean Iou: 0.8381
Mean Accuracy: 0.9102
Overall Accuracy: 0.9168
Accuracy Not Graf: 0.9379
Accuracy Graf: 0.8826
Iou Not Graf: 0.8748
Iou Graf: 0.8015

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: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.05
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Not Graf	Accuracy Graf	Iou Not Graf	Iou Graf
0.4076	0.42	20	0.5389	0.6053	0.7982	0.7541	0.6139	0.9825	0.6073	0.6033
0.3386	0.83	40	0.2883	0.7962	0.8984	0.8898	0.8625	0.9343	0.8290	0.7634
0.1964	1.25	60	0.2514	0.8061	0.9009	0.8964	0.8819	0.9200	0.8406	0.7716
0.1723	1.67	80	0.2259	0.8269	0.9058	0.9100	0.9235	0.8880	0.8641	0.7898
0.1981	2.08	100	0.2338	0.8119	0.9040	0.8999	0.8869	0.9210	0.8459	0.7778
0.2827	2.5	120	0.2106	0.8251	0.9080	0.9084	0.9095	0.9066	0.8601	0.7902
0.1864	2.92	140	0.2241	0.8232	0.8956	0.9097	0.9546	0.8365	0.8675	0.7790
0.1362	3.33	160	0.2185	0.8257	0.8978	0.9109	0.9525	0.8431	0.8688	0.7826
0.1264	3.75	180	0.2155	0.8237	0.9054	0.9079	0.9156	0.8952	0.8602	0.7871
0.1688	4.17	200	0.2241	0.8206	0.8985	0.9072	0.9346	0.8625	0.8618	0.7795
0.1198	4.58	220	0.2080	0.8331	0.9087	0.9137	0.9296	0.8877	0.8697	0.7965
0.111	5.0	240	0.2033	0.8369	0.9133	0.9154	0.9221	0.9044	0.8710	0.8027
0.2003	5.42	260	0.2214	0.8262	0.9118	0.9084	0.8976	0.9261	0.8586	0.7938
0.1369	5.83	280	0.2044	0.8396	0.9147	0.9170	0.9245	0.9048	0.8734	0.8058
0.1901	6.25	300	0.1968	0.8411	0.9119	0.9185	0.9393	0.8846	0.8771	0.8050
0.1887	6.67	320	0.2098	0.8367	0.9100	0.9159	0.9344	0.8857	0.8731	0.8002
0.0738	7.08	340	0.2205	0.8357	0.9127	0.9147	0.9211	0.9043	0.8699	0.8014
0.1166	7.5	360	0.2274	0.8317	0.9046	0.9135	0.9420	0.8672	0.8709	0.7924
0.1247	7.92	380	0.2225	0.8310	0.9051	0.9130	0.9381	0.8722	0.8698	0.7923
0.1212	8.33	400	0.2230	0.8345	0.9108	0.9143	0.9254	0.8961	0.8699	0.7991
0.0979	8.75	420	0.2226	0.8352	0.9076	0.9153	0.9400	0.8752	0.8730	0.7973
0.0984	9.17	440	0.2189	0.8354	0.9106	0.9149	0.9287	0.8925	0.8712	0.7997
0.1151	9.58	460	0.2185	0.8382	0.9098	0.9170	0.9396	0.8800	0.8751	0.8013
0.0989	10.0	480	0.2171	0.8381	0.9102	0.9168	0.9379	0.8826	0.8748	0.8015

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2