README.md

metadata

license: other
base_model: nvidia/mit-b0
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: segformer-b0-finetuned-segments-greenhouse-oct-23
    results: []
widget:
  - src: >-
      https://european-seed.com/wp-content/uploads/2020/04/IMG_1480-2-scaled-1-2048x1536.jpg
    example_title: sample for internet
  - src: >-
      https://raw.githubusercontent.com/mikeagz/portfolio/main/assets/img/sample.jpg
    example_title: sample for train dataset

segformer-b0-finetuned-segments-greenhouse-oct-23

This model is a fine-tuned version of nvidia/mit-b0 on the MexicanVanGogh/greenhouse dataset. It achieves the following results on the evaluation set:

• Loss: 0.7058
• Mean Iou: 0.2227
• Mean Accuracy: 0.2804
• Overall Accuracy: 0.9101
• Accuracy Unlabeled: nan
• Accuracy Object: nan
• Accuracy Road: 0.9378
• Accuracy Plant: 0.9667
• Accuracy Iron: 0.0
• Accuracy Wood: 0.0
• Accuracy Wall: 0.1932
• Accuracy Raw Road: nan
• Accuracy Bottom Wall: 0.0
• Accuracy Roof: 0.1457
• Accuracy Grass: 0.0
• Iou Unlabeled: nan
• Iou Object: nan
• Iou Road: 0.9039
• Iou Plant: 0.8421
• Iou Iron: 0.0
• Iou Wood: 0.0
• Iou Wall: 0.1521
• Iou Raw Road: 0.0
• Iou Bottom Wall: 0.0
• Iou Roof: 0.1061
• Iou Grass: 0.0

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: 6e-05
• 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
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Object	Accuracy Road	Accuracy Plant	Accuracy Iron	Accuracy Wood	Accuracy Wall	Accuracy Raw Road	Accuracy Bottom Wall	Accuracy Roof	Accuracy Grass	Iou Unlabeled	Iou Object	Iou Road	Iou Plant	Iou Iron	Iou Wood	Iou Wall	Iou Raw Road	Iou Bottom Wall	Iou Roof	Iou Grass
1.8756	2.86	20	2.0063	0.1415	0.2269	0.8216	nan	nan	0.7882	0.9674	0.0	0.0594	0.0	nan	0.0	0.0	0.0	0.0	0.0	0.7760	0.7552	0.0	0.0256	0.0	0.0	0.0	0.0	0.0
1.3624	5.71	40	1.0910	0.1715	0.2380	0.8991	nan	nan	0.9206	0.9757	0.0	0.0077	0.0	nan	0.0	0.0	0.0	0.0	nan	0.8888	0.8220	0.0	0.0045	0.0	0.0	0.0	0.0	0.0
1.4095	8.57	60	0.9033	0.1734	0.2392	0.9068	nan	nan	0.9264	0.9873	0.0	0.0	0.0	nan	0.0	0.0	0.0	0.0	nan	0.9000	0.8338	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.8802	11.43	80	0.7784	0.1764	0.2414	0.9165	nan	nan	0.9470	0.9823	0.0	0.0022	0.0	nan	0.0	0.0	0.0	0.0	nan	0.9155	0.8463	0.0	0.0021	0.0	0.0	0.0	0.0	0.0
1.0936	14.29	100	0.8060	0.1946	0.2405	0.9132	nan	nan	0.9400	0.9839	0.0	0.0	0.0	nan	0.0	0.0	0.0	nan	nan	0.9100	0.8418	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.8086	17.14	120	0.7786	0.1940	0.2402	0.9115	nan	nan	0.9361	0.9852	0.0	0.0	0.0	nan	0.0	0.0006	0.0	nan	nan	0.9071	0.8380	0.0	0.0	0.0	0.0	0.0	0.0006	0.0
1.0669	20.0	140	0.7462	0.2072	0.2562	0.9088	nan	nan	0.9282	0.9853	0.0	0.0	0.0113	nan	0.0	0.1246	0.0	nan	nan	0.9010	0.8385	0.0	0.0	0.0102	0.0	0.0	0.1155	0.0
0.7399	22.86	160	0.7328	0.2137	0.2662	0.9080	nan	nan	0.9290	0.9788	0.0	0.0	0.0814	nan	0.0	0.1405	0.0	nan	nan	0.8997	0.8389	0.0	0.0	0.0663	0.0	0.0	0.1181	0.0
0.808	25.71	180	0.7296	0.2218	0.2797	0.9072	nan	nan	0.9277	0.9742	0.0	0.0	0.1840	nan	0.0	0.1515	0.0	nan	nan	0.8981	0.8404	0.0	0.0	0.1423	0.0	0.0	0.1155	0.0
0.8494	28.57	200	0.7058	0.2227	0.2804	0.9101	nan	nan	0.9378	0.9667	0.0	0.0	0.1932	nan	0.0	0.1457	0.0	nan	nan	0.9039	0.8421	0.0	0.0	0.1521	0.0	0.0	0.1061	0.0

Framework versions

• Transformers 4.34.1
• Pytorch 2.1.0+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1