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segformer-b0-scene-parse-150
LYJ123123/segformer-b0-scene-parse-150
segformer-b0-scene-parse-150 is a machine learning model from LYJ123123. Use it for the machine learning 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
- Library
- transformers
- License
- other
- Model type
- segformer
- Access
- Public
- Created
- Aug 9, 2023
- Updated
- Aug 9, 2023
- SHA
- 905894f7
Base models
Datasets
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-scene-parse-150
This model is a fine-tuned version of nvidia/mit-b0 on the scene_parse_150 dataset. It achieves the following results on the evaluation set:
- Loss: 4.9393
- Mean Iou: 0.0036
- Mean Accuracy: 0.0214
- Overall Accuracy: 0.0867
- Per Category Iou: [0.16545709180085544, 0.0, 0.0, 0.0, 0.0, 0.058472783227543755, nan, 0.0, 0.0, 0.0, 0.007622227522060578, nan, 3.137911197113122e-05, 0.0, 0.058198708972300964, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.041340794105739556, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0024778587375187066, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.016656203154428628, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0007263579350175389, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0697279103015839, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.012292855202390655, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0]
- Per Category Accuracy: [0.18326833008776816, nan, 0.0, 0.0, 0.0, 0.09695526450076544, nan, nan, 0.0, nan, 0.009522447471605468, nan, 0.0035169988276670576, 0.0, 0.06740772973614463, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.07055362102652567, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0025769907891715358, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.018805149717922753, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0010196214966054064, nan, nan, nan, nan, nan, nan, 0.23142163272931066, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.019714628036161638, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan]
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|---|---|---|---|---|---|---|---|---|
| 4.8574 | 1.0 | 20 | 4.9393 | 0.0036 | 0.0214 | 0.0867 | [0.16545709180085544, 0.0, 0.0, 0.0, 0.0, 0.058472783227543755, nan, 0.0, 0.0, 0.0, 0.007622227522060578, nan, 3.137911197113122e-05, 0.0, 0.058198708972300964, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.041340794105739556, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0024778587375187066, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.016656203154428628, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0007263579350175389, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0697279103015839, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.012292855202390655, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0] | [0.18326833008776816, nan, 0.0, 0.0, 0.0, 0.09695526450076544, nan, nan, 0.0, nan, 0.009522447471605468, nan, 0.0035169988276670576, 0.0, 0.06740772973614463, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.07055362102652567, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0025769907891715358, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.018805149717922753, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0010196214966054064, nan, nan, nan, nan, nan, nan, 0.23142163272931066, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.019714628036161638, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan] |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- Aug 9, 2023
- Updated
- Aug 9, 2023