Segformer B0 Mars Testbed Fewshot
This model is a fine-tuned version of nvidia/mit-b0 on the Tani04/marstestbedterrain dataset. It achieves the following results on the evaluation set: Loss: 5.5421 Mean Iou: 0.3524 Mean Accuracy: 0.4470 Overall...
Source du modèle
- Type
- Other
- Taille du fichier
- 14,2 MB
- Modèle de base
- nvidia/mit-b0
- Version
- vRevision 740f8db
- Mis à jour
- 16 sept. 2026
- Téléchargements
- 3
- Source
- Hugging Face
Description de la source
segformer-b0-mars-testbed-fewshot
This model is a fine-tuned version of nvidia/mit-b0 on the Tani04/mars_testbed_terrain dataset. It achieves the following results on the evaluation set:
- Loss: 5.5421
- Mean Iou: 0.3524
- Mean Accuracy: 0.4470
- Overall Accuracy: 0.6053
- Accuracy Flat Bedrock: 0.0
- Accuracy Flat Gravel: 0.8431
- Accuracy Hard Gravel: 0.1552
- Accuracy Obstacle: 0.7895
- Iou Flat Bedrock: 0.0
- Iou Flat Gravel: 0.7604
- Iou Hard Gravel: 0.1121
- Iou Obstacle: 0.5371
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 60
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Flat Bedrock | Accuracy Flat Gravel | Accuracy Hard Gravel | Accuracy Obstacle | Iou Flat Bedrock | Iou Flat Gravel | Iou Hard Gravel | Iou Obstacle |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.9524 | 2.5 | 20 | 2.7762 | 0.2467 | 0.4256 | 0.5007 | 0.0 | 0.9625 | 0.1759 | 0.5642 | 0.0 | 0.4426 | 0.1131 | 0.4309 |
| 1.9458 | 5.0 | 40 | 3.0021 | 0.3148 | 0.4469 | 0.5622 | 0.0 | 0.9322 | 0.1733 | 0.6820 | 0.0 | 0.6568 | 0.1131 | 0.4893 |
| 0.5475 | 7.5 | 60 | 3.3026 | 0.3227 | 0.4611 | 0.5852 | 0.0 | 0.9585 | 0.1652 | 0.7207 | 0.0 | 0.6585 | 0.1147 | 0.5174 |
| 0.3696 | 10.0 | 80 | 3.7134 | 0.3445 | 0.4580 | 0.6020 | 0.0 | 0.9118 | 0.1511 | 0.7693 | 0.0 | 0.7352 | 0.1082 | 0.5347 |
| 0.3826 | 12.5 | 100 | 4.1692 | 0.3456 | 0.4555 | 0.5962 | 0.0 | 0.9079 | 0.1575 | 0.7565 | 0.0 | 0.7465 | 0.1107 | 0.5252 |
| 0.2651 | 15.0 | 120 | 4.1228 | 0.3453 | 0.4423 | 0.5933 | 0.0 | 0.8489 | 0.1522 | 0.7683 | 0.0 | 0.7503 | 0.1071 | 0.5237 |
| 0.3288 | 17.5 | 140 | 4.2263 | 0.3515 | 0.4577 | 0.6159 | 0.0 | 0.8787 | 0.1492 | 0.8030 | 0.0 | 0.7429 | 0.1106 | 0.5527 |
| 0.1629 | 20.0 | 160 | 4.6687 | 0.3453 | 0.4441 | 0.5827 | 0.0 | 0.8782 | 0.1597 | 0.7385 | 0.0 | 0.7653 | 0.1086 | 0.5073 |
| 0.377 | 22.5 | 180 | 4.5768 | 0.3475 | 0.4440 | 0.5953 | 0.0 | 0.8478 | 0.1601 | 0.7680 | 0.0 | 0.7527 | 0.1124 | 0.5250 |
| 0.3951 | 25.0 | 200 | 4.6531 | 0.3393 | 0.4327 | 0.5730 | 0.0 | 0.8427 | 0.1562 | 0.7317 | 0.0 | 0.7553 | 0.1053 | 0.4965 |
| 0.1941 | 27.5 | 220 | 4.5228 | 0.3530 | 0.4543 | 0.6116 | 0.0 | 0.8698 | 0.1508 | 0.7966 | 0.0 | 0.7556 | 0.1104 | 0.5462 |
| 0.237 | 30.0 | 240 | 4.5266 | 0.3526 | 0.4541 | 0.6031 | 0.0 | 0.8839 | 0.1580 | 0.7744 | 0.0 | 0.7650 | 0.1128 | 0.5325 |
| 0.2397 | 32.5 | 260 | 5.0232 | 0.3486 | 0.4460 | 0.5950 | 0.0 | 0.8608 | 0.1578 | 0.7656 | 0.0 | 0.7606 | 0.1111 | 0.5229 |
| 0.1909 | 35.0 | 280 | 5.4829 | 0.3564 | 0.4563 | 0.6200 | 0.0 | 0.8635 | 0.1462 | 0.8155 | 0.0 | 0.7600 | 0.1102 | 0.5554 |
| 0.1391 | 37.5 | 300 | 5.3365 | 0.3553 | 0.4581 | 0.6153 | 0.0 | 0.8811 | 0.1515 | 0.8000 | 0.0 | 0.7610 | 0.1123 | 0.5481 |
| 0.2368 | 40.0 | 320 | 5.4860 | 0.3527 | 0.4525 | 0.6051 | 0.0 | 0.8730 | 0.1551 | 0.7820 | 0.0 | 0.7635 | 0.1116 | 0.5357 |
| 0.1269 | 42.5 | 340 | 5.1693 | 0.3508 | 0.4462 | 0.5977 | 0.0 | 0.8546 | 0.1590 | 0.7712 | 0.0 | 0.7646 | 0.1124 | 0.5263 |
| 0.0917 | 45.0 | 360 | 5.3999 | 0.3552 | 0.4541 | 0.6138 | 0.0 | 0.8633 | 0.1517 | 0.8015 | 0.0 | 0.7618 | 0.1119 | 0.5471 |
| 2.1454 | 47.5 | 380 | 6.0907 | 0.3528 | 0.4518 | 0.6062 | 0.0 | 0.8685 | 0.1520 | 0.7867 | 0.0 | 0.7640 | 0.1102 | 0.5371 |
| 0.1348 | 50.0 | 400 | 5.6524 | 0.3541 | 0.4487 | 0.6087 | 0.0 | 0.8456 | 0.1535 | 0.7958 | 0.0 | 0.7633 | 0.1119 | 0.5411 |
| 0.1323 | 52.5 | 420 | 5.7016 | 0.3532 | 0.4492 | 0.6062 | 0.0 | 0.8531 | 0.1549 | 0.7888 | 0.0 | 0.7632 | 0.1120 | 0.5376 |
| 0.1165 | 55.0 | 440 | 5.6639 | 0.3502 | 0.4419 | 0.6033 | 0.0 | 0.8218 | 0.1545 | 0.7913 | 0.0 | 0.7538 | 0.1115 | 0.5355 |
| 0.1574 | 57.5 | 460 | 5.8704 | 0.3532 | 0.4474 | 0.6085 | 0.0 | 0.8378 | 0.1554 | 0.7963 | 0.0 | 0.7585 | 0.1132 | 0.5411 |
| 0.0974 | 60.0 | 480 | 5.5421 | 0.3524 | 0.4470 | 0.6053 | 0.0 | 0.8431 | 0.1552 | 0.7895 | 0.0 | 0.7604 | 0.1121 | 0.5371 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
Sources
1 source
Sources
1 sourceVérifié 1 sept.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
2 extraits
Extraits de sources
2 extraits--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - mars-terrain - few-shot-learning - generated_from_trainer model-index: - name: segformer-b0-mars-testbed-fewshot results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-mars-testbed-fewshot This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Tani04/mars_testbed_terrain dataset. It achieves the following results on the evaluation set: - Loss: 5.5421 - Mean Iou: 0.3524 - Mean Accuracy: 0.4470 - Overall Accuracy: 0.6053 - Accuracy Flat Bedrock: 0.0 - Accuracy Flat Gravel: 0.8431 - Accuracy Hard Gravel: 0.1552 - Accuracy Obstacle: 0.7895 - Iou Flat Bedrock: 0.0 - Iou Flat Gravel: 0.7604 - Iou Hard Gravel: 0.1121 - Iou Obstacle: 0.5371 ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Flat Bedrock | Accuracy Flat Gravel | Accuracy Hard Gravel | Accuracy Obstacle | Iou Flat Bedrock | Iou Flat Gravel | Iou Hard Gravel | Iou Obstacle | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------:|:--------------------:|:--------------------:|:-----------------:|:----------------:|:---------------:|:---------------:|:------------:| | 0.9524 | 2.5 | 20 | 2.7762 | 0.2467 | 0.4256 | 0.5007 | 0.0 | 0.9625 | 0.1759 | 0.5642 | 0.0 | 0.4426 | 0.1131 | 0.4309 | | 1.9458 | 5.0 | 40 | 3.0021 | 0.3148 | 0.4469 | 0.5622 | 0.0 | 0.9322 | 0.1733 | 0.6820 | 0.0 | 0.6568 | 0.1131 | 0.4893 | | 0.5475 | 7.5 | 60 | 3.3026 | 0.3227 | 0.4611 | 0.5852 | 0.0 | 0.9585 | 0.1652 | 0.7207 | 0.0 | 0.6585 | 0.1147 | 0.5174 | | 0.3696...
Source context: 3 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo Tani04/segformer-b0-mars-testbed-fewshot
