---
license: apache-2.0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-Maize-10k-steps-sem
  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-finetuned-Maize-10k-steps-sem

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the koushikn/Maize_sem_seg dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0756
- Mean Iou: 0.9172
- Mean Accuracy: 0.9711
- Overall Accuracy: 0.9804
- Accuracy Background: 0.9834
- Accuracy Maize: 0.9588
- Iou Background: 0.9779
- Iou Maize: 0.8566

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Maize | Iou Background | Iou Maize |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.0529        | 1.0   | 678   | 69.3785         | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.3755        | 2.0   | 1356  | 0.9455          | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0603        | 3.0   | 2034  | 0.0920          | 0.8356   | 0.8602        | 0.9641           | 0.9976              | 0.7227         | 0.9607         | 0.7106    |
| 0.0341        | 4.0   | 2712  | 24.6203         | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0332        | 5.0   | 3390  | 101.5635        | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0331        | 6.0   | 4068  | 9.6824          | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0302        | 7.0   | 4746  | 260.7923        | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0305        | 8.0   | 5424  | 172.8153        | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0313        | 9.0   | 6102  | 304.2714        | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0301        | 10.0  | 6780  | 547.2355        | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.03          | 11.0  | 7458  | 224.2607        | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0285        | 12.0  | 8136  | 116.3474        | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0284        | 13.0  | 8814  | 96.8429         | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.0281        | 14.0  | 9492  | 54.2593         | 0.4391   | 0.5           | 0.8781           | 1.0                 | 0.0            | 0.8781         | 0.0       |
| 0.028         | 14.75 | 10000 | 0.0756          | 0.9172   | 0.9711        | 0.9804           | 0.9834              | 0.9588         | 0.9779         | 0.8566    |


### Framework versions

- Transformers 4.21.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1