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segformer-b0-finetuned-segments-sidewalk-oct-22
mobthomas/segformer-b0-finetuned-segments-sidewalk-oct-22
segformer-b0-finetuned-segments-sidewalk-oct-22 is a image segmentation model from mobthomas. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Segmentation
- Library
- transformers
- License
- other
- Model type
- segformer
- Access
- Public
- Created
- Mar 14, 2024
- Updated
- Mar 14, 2024
- SHA
- 74d18ad5
Base models
Model card
From the Hugging Face model README
<!-- 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-finetuned-segments-sidewalk-oct-22
This model is a fine-tuned version of nvidia/mit-b0 on the mobthomas/warthunder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9792
- Mean Iou: 0.0964
- Mean Accuracy: 0.2300
- Overall Accuracy: 0.2636
- Accuracy Unlabeled: nan
- Accuracy Tank: 0.4595
- Accuracy Artillery: nan
- Accuracy Apc: 0.0005
- Iou Unlabeled: 0.0
- Iou Tank: 0.2885
- Iou Artillery: nan
- Iou Apc: 0.0005
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: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Tank | Accuracy Artillery | Accuracy Apc | Iou Unlabeled | Iou Tank | Iou Artillery | Iou Apc |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.9221 | 10.0 | 20 | 1.2904 | 0.1235 | 0.4284 | 0.4911 | nan | 0.8568 | nan | 0.0 | 0.0 | 0.4942 | 0.0 | 0.0 |
| 0.7631 | 20.0 | 40 | 1.0716 | 0.1194 | 0.2947 | 0.3378 | nan | 0.5887 | nan | 0.0008 | 0.0 | 0.3575 | nan | 0.0008 |
| 0.6631 | 30.0 | 60 | 0.9722 | 0.1060 | 0.2463 | 0.2818 | nan | 0.4888 | nan | 0.0038 | 0.0 | 0.3143 | nan | 0.0038 |
| 0.5846 | 40.0 | 80 | 0.9368 | 0.0827 | 0.1925 | 0.2207 | nan | 0.3850 | nan | 0.0 | 0.0 | 0.2481 | nan | 0.0 |
| 0.5662 | 50.0 | 100 | 0.9792 | 0.0964 | 0.2300 | 0.2636 | nan | 0.4595 | nan | 0.0005 | 0.0 | 0.2885 | nan | 0.0005 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Image Segmentation
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- Mar 14, 2024
- Updated
- Mar 14, 2024