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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
base_model: nvidia/mit-b0
model-index:
- name: segformer-b0-finetuned-human-parsing
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-finetuned-human-parsing
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9476
- Mean Iou: 0.0726
- Mean Accuracy: 0.1221
- Overall Accuracy: 0.3575
- Accuracy Background: nan
- Accuracy Hat: 0.0048
- Accuracy Hair: 0.4813
- Accuracy Sunglasses: 0.0
- Accuracy Upper-clothes: 0.9405
- Accuracy Skirt: 0.0000
- Accuracy Pants: 0.0631
- Accuracy Dress: 0.1031
- Accuracy Belt: 0.0
- Accuracy Left-shoe: 0.0011
- Accuracy Right-shoe: 0.0010
- Accuracy Face: 0.4406
- Accuracy Left-leg: 0.0291
- Accuracy Right-leg: 0.0
- Accuracy Left-arm: 0.0
- Accuracy Right-arm: 0.0001
- Accuracy Bag: 0.0114
- Accuracy Scarf: 0.0
- Iou Background: 0.0
- Iou Hat: 0.0043
- Iou Hair: 0.4221
- Iou Sunglasses: 0.0
- Iou Upper-clothes: 0.3239
- Iou Skirt: 0.0000
- Iou Pants: 0.0559
- Iou Dress: 0.0728
- Iou Belt: 0.0
- Iou Left-shoe: 0.0011
- Iou Right-shoe: 0.0009
- Iou Face: 0.3872
- Iou Left-leg: 0.0271
- Iou Right-leg: 0.0
- Iou Left-arm: 0.0
- Iou Right-arm: 0.0001
- Iou Bag: 0.0106
- Iou Scarf: 0.0
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Hat | Accuracy Hair | Accuracy Sunglasses | Accuracy Upper-clothes | Accuracy Skirt | Accuracy Pants | Accuracy Dress | Accuracy Belt | Accuracy Left-shoe | Accuracy Right-shoe | Accuracy Face | Accuracy Left-leg | Accuracy Right-leg | Accuracy Left-arm | Accuracy Right-arm | Accuracy Bag | Accuracy Scarf | Iou Background | Iou Hat | Iou Hair | Iou Sunglasses | Iou Upper-clothes | Iou Skirt | Iou Pants | Iou Dress | Iou Belt | Iou Left-shoe | Iou Right-shoe | Iou Face | Iou Left-leg | Iou Right-leg | Iou Left-arm | Iou Right-arm | Iou Bag | Iou Scarf |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------:|:-------------------:|:----------------------:|:--------------:|:--------------:|:--------------:|:-------------:|:------------------:|:-------------------:|:-------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:------------:|:--------------:|:--------------:|:-------:|:--------:|:--------------:|:-----------------:|:---------:|:---------:|:---------:|:--------:|:-------------:|:--------------:|:--------:|:------------:|:-------------:|:------------:|:-------------:|:-------:|:---------:|
| 2.5768 | 0.4 | 20 | 2.7812 | 0.0726 | 0.1332 | 0.2876 | nan | 0.0178 | 0.3204 | 0.0004 | 0.5548 | 0.0004 | 0.2555 | 0.2373 | 0.0 | 0.0103 | 0.0003 | 0.5637 | 0.0287 | 0.0302 | 0.0001 | 0.0008 | 0.2435 | 0.0 | 0.0 | 0.0166 | 0.2759 | 0.0001 | 0.2781 | 0.0004 | 0.1710 | 0.1295 | 0.0 | 0.0098 | 0.0003 | 0.3251 | 0.0260 | 0.0248 | 0.0001 | 0.0007 | 0.0491 | 0.0 |
| 2.2093 | 0.8 | 40 | 2.5166 | 0.0563 | 0.1052 | 0.3288 | nan | 0.0 | 0.1994 | 0.0 | 0.9447 | 0.0015 | 0.0435 | 0.1164 | 0.0 | 0.0008 | 0.0000 | 0.4655 | 0.0007 | 0.0003 | 0.0 | 0.0 | 0.0153 | 0.0 | 0.0 | 0.0 | 0.1946 | 0.0 | 0.3037 | 0.0015 | 0.0417 | 0.0842 | 0.0 | 0.0008 | 0.0000 | 0.3726 | 0.0007 | 0.0003 | 0.0 | 0.0 | 0.0124 | 0.0 |
| 1.8804 | 1.2 | 60 | 2.0209 | 0.0632 | 0.1110 | 0.3374 | nan | 0.0087 | 0.3724 | 0.0 | 0.9475 | 0.0014 | 0.0162 | 0.0528 | 0.0 | 0.0001 | 0.0008 | 0.4257 | 0.0561 | 0.0001 | 0.0 | 0.0 | 0.0055 | 0.0 | 0.0 | 0.0077 | 0.3472 | 0.0 | 0.3086 | 0.0014 | 0.0156 | 0.0403 | 0.0 | 0.0001 | 0.0008 | 0.3597 | 0.0515 | 0.0001 | 0.0 | 0.0 | 0.0052 | 0.0 |
| 1.8776 | 1.6 | 80 | 2.0016 | 0.0665 | 0.1154 | 0.3454 | nan | 0.0056 | 0.4172 | 0.0 | 0.9412 | 0.0000 | 0.0490 | 0.0697 | 0.0 | 0.0002 | 0.0006 | 0.4349 | 0.0329 | 0.0000 | 0.0 | 0.0000 | 0.0100 | 0.0 | 0.0 | 0.0048 | 0.3791 | 0.0 | 0.3138 | 0.0000 | 0.0438 | 0.0542 | 0.0 | 0.0002 | 0.0006 | 0.3608 | 0.0304 | 0.0000 | 0.0 | 0.0000 | 0.0093 | 0.0 |
| 1.8471 | 2.0 | 100 | 1.9476 | 0.0726 | 0.1221 | 0.3575 | nan | 0.0048 | 0.4813 | 0.0 | 0.9405 | 0.0000 | 0.0631 | 0.1031 | 0.0 | 0.0011 | 0.0010 | 0.4406 | 0.0291 | 0.0 | 0.0 | 0.0001 | 0.0114 | 0.0 | 0.0 | 0.0043 | 0.4221 | 0.0 | 0.3239 | 0.0000 | 0.0559 | 0.0728 | 0.0 | 0.0011 | 0.0009 | 0.3872 | 0.0271 | 0.0 | 0.0 | 0.0001 | 0.0106 | 0.0 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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