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segformer-b0-scene-parse-150
ArturR01/segformer-b0-scene-parse-150
segformer-b0-scene-parse-150 is a machine learning model from ArturR01. 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
- May 23, 2023
- Updated
- May 23, 2023
- SHA
- 7715dd14
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: 4.4530
- Mean Iou: 0.0308
- Mean Accuracy: 0.0934
- Overall Accuracy: 0.3126
- Per Category Iou: [0.368405958754407, 0.11499370080653983, 0.5753658515502771, 0.2138805564642673, 0.28958703459911295, 0.191305743989082, 0.003497854077253219, 0.1288281531360376, 0.12360856380177596, 0.0, 0.0, 0.0, 0.003947940713975041, 0.0, 0.0, 0.015025862437481299, nan, 0.0, 0.0, 0.0037038152308109247, 4.4974139869574995e-05, nan, 0.12424162490108151, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.24922118380062305, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan]
- Per Category Accuracy: [0.7566432234358786, 0.24871206280227098, 0.7073059287949548, 0.34911440750830386, 0.992694013910948, 0.2160975230593844, 0.0035416689300031504, 0.5627543803943077, 0.22603353810393492, 0.0, 0.0, nan, 0.17717717717717718, nan, 0.0, 0.017564022485946285, nan, 0.0, 0.0, 0.004741894444658622, 0.004261363636363636, nan, 0.19470855725506409, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.3939161833898676, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 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 |
|---|---|---|---|---|---|---|---|---|
| 4.6085 | 1.0 | 20 | 4.4530 | 0.0308 | 0.0934 | 0.3126 | [0.368405958754407, 0.11499370080653983, 0.5753658515502771, 0.2138805564642673, 0.28958703459911295, 0.191305743989082, 0.003497854077253219, 0.1288281531360376, 0.12360856380177596, 0.0, 0.0, 0.0, 0.003947940713975041, 0.0, 0.0, 0.015025862437481299, nan, 0.0, 0.0, 0.0037038152308109247, 4.4974139869574995e-05, nan, 0.12424162490108151, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.24922118380062305, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan] | [0.7566432234358786, 0.24871206280227098, 0.7073059287949548, 0.34911440750830386, 0.992694013910948, 0.2160975230593844, 0.0035416689300031504, 0.5627543803943077, 0.22603353810393492, 0.0, 0.0, nan, 0.17717717717717718, nan, 0.0, 0.017564022485946285, nan, 0.0, 0.0, 0.004741894444658622, 0.004261363636363636, nan, 0.19470855725506409, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.3939161833898676, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan] |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- May 23, 2023
- Updated
- May 23, 2023