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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
