transformers tensorboard safetensors segformer generated_from_trainer base_model:klentree/segformer-b0-scene-parse-150 license:openrail endpoints_compatible region:us
---
license: openrail
base_model: klentree/segformer-b0-scene-parse-150
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-scene-parse-150-20
  results: []
---

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# segformer-b0-scene-parse-150-20

This model is a fine-tuned version of [klentree/segformer-b0-scene-parse-150](https://huggingface.co/klentree/segformer-b0-scene-parse-150) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9683
- Mean Iou: 0.2488
- Mean Accuracy: 0.6439
- Overall Accuracy: 0.4669
- Per Category Iou: [0.044136660707036465, 0.4534135035032552]
- Per Category Accuracy: [0.8321388577827548, 0.45573371779771177]

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                             | Per Category Accuracy                      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------:|:------------------------------------------:|
| No log        | 1.0   | 133  | 0.1391          | 0.5047   | 0.5218        | 0.9675           | [0.04196317239808363, 0.967443434576684]     | [0.04813532865044623, 0.9955161397039883]  |
| No log        | 2.0   | 266  | 0.1449          | 0.5154   | 0.5451        | 0.9597           | [0.07117236629542235, 0.9595300984781853]    | [0.10450134039159795, 0.9857244117018465]  |
| No log        | 3.0   | 399  | 0.1669          | 0.5128   | 0.5453        | 0.9569           | [0.06888629524029359, 0.9567357547653306]    | [0.10785231938647392, 0.9827560505657416]  |
| 0.3733        | 4.0   | 532  | 0.2122          | 0.5112   | 0.5576        | 0.9478           | [0.07483006503985525, 0.9475505125490813]    | [0.14279581933557298, 0.9723116234544236]  |
| 0.3733        | 5.0   | 665  | 0.2619          | 0.5036   | 0.6005        | 0.9199           | [0.0879079186151223, 0.9192549826254068]     | [0.2610336285588245, 0.9399632680244574]   |
| 0.3733        | 6.0   | 798  | 0.3013          | 0.4815   | 0.6494        | 0.8758           | [0.08872990969490727, 0.8742854201811897]    | [0.40877362652278665, 0.8900430360382596]  |
| 0.3733        | 7.0   | 931  | 0.3093          | 0.4823   | 0.6044        | 0.8905           | [0.0750589475389164, 0.8895250315242322]     | [0.30035460992907803, 0.9084973158596108]  |
| 0.2604        | 8.0   | 1064 | 0.2087          | 0.5090   | 0.5805        | 0.9342           | [0.0842029042177912, 0.9337687360922476]     | [0.20460314228511317, 0.9564103131735049]  |
| 0.2604        | 9.0   | 1197 | 0.4990          | 0.4044   | 0.6864        | 0.7459           | [0.06763197122282019, 0.7411177869667952]    | [0.6230818826563508, 0.7496334354079776]   |
| 0.2604        | 10.0  | 1330 | 0.3745          | 0.4472   | 0.6666        | 0.8206           | [0.07657083870114431, 0.8178660489858295]    | [0.5029200176456615, 0.830259490697111]    |
| 0.2604        | 11.0  | 1463 | 1.2307          | 0.1467   | 0.5701        | 0.2778           | [0.03481955130980837, 0.25848886923646197]   | [0.8806949675930639, 0.2594289909506597]   |
| 0.1761        | 12.0  | 1596 | 0.2577          | 0.4905   | 0.6244        | 0.8952           | [0.0867593572032901, 0.8941645624438329]     | [0.33649258542875565, 0.9122507365946599]  |
| 0.1761        | 13.0  | 1729 | 0.4025          | 0.4353   | 0.6588        | 0.8030           | [0.07054198842750235, 0.7999897274079024]    | [0.505458278190641, 0.8120503803644273]    |
| 0.1761        | 14.0  | 1862 | 5.8258          | 0.0188   | 0.5025        | 0.0372           | [0.029723375945066684, 0.007894678045920561] | [0.9970477450880586, 0.007895388558467544] |
| 0.1761        | 15.0  | 1995 | 0.7888          | 0.3005   | 0.6622        | 0.5618           | [0.049354828101168945, 0.5516162745499266]   | [0.7689979300281652, 0.5555007860940694]   |
| 0.1331        | 16.0  | 2128 | 0.3346          | 0.4627   | 0.6483        | 0.8488           | [0.07848367609919223, 0.846876325384084]     | [0.43516067732193153, 0.8614587046651099]  |
| 0.1331        | 17.0  | 2261 | 2.7924          | 0.0254   | 0.5076        | 0.0497           | [0.03002291425841882, 0.02085235460380035]   | [0.9943313312294275, 0.0208559580633969]   |
| 0.1331        | 18.0  | 2394 | 1.5711          | 0.1377   | 0.5818        | 0.2605           | [0.03562331490097667, 0.23971062459649647]   | [0.9234127388102752, 0.24027028849931653]  |
| 0.1234        | 19.0  | 2527 | 0.2332          | 0.4998   | 0.6022        | 0.9146           | [0.08556669218462386, 0.9139353359496684]    | [0.2700549730224982, 0.9342724393702061]   |
| 0.1234        | 20.0  | 2660 | 0.9683          | 0.2488   | 0.6439        | 0.4669           | [0.044136660707036465, 0.4534135035032552]   | [0.8321388577827548, 0.45573371779771177]  |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
