---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-agriculture
  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-agriculture

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: 0.3305
- Mean Iou: 0.4242
- Mean Accuracy: 0.5107
- Overall Accuracy: 0.6733
- Accuracy Unlabeled: nan
- Accuracy Nutrient Deficiency: 0.6872
- Accuracy Planter Skip: 0.1915
- Accuracy Water: 0.8549
- Accuracy Waterway: 0.1797
- Accuracy Weed Cluster: 0.6404
- Iou Unlabeled: 0.0
- Iou Nutrient Deficiency: 0.6865
- Iou Planter Skip: 0.1914
- Iou Water: 0.8475
- Iou Waterway: 0.1795
- Iou Weed Cluster: 0.6401

## 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: 1e-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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Nutrient Deficiency | Accuracy Planter Skip | Accuracy Water | Accuracy Waterway | Accuracy Weed Cluster | Iou Unlabeled | Iou Nutrient Deficiency | Iou Planter Skip | Iou Water | Iou Waterway | Iou Weed Cluster |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------------------:|:---------------------:|:--------------:|:-----------------:|:---------------------:|:-------------:|:-----------------------:|:----------------:|:---------:|:------------:|:----------------:|
| 0.2889        | 1.0   | 8145  | 0.4127          | 0.2578   | 0.3110        | 0.4484           | nan                | 0.3062                       | 0.0                   | 0.7988         | 0.0007            | 0.4496                | 0.0           | 0.3062                  | 0.0              | 0.7913    | 0.0007       | 0.4485           |
| 0.3157        | 2.0   | 16290 | 0.3877          | 0.3374   | 0.4070        | 0.5970           | nan                | 0.5241                       | 0.0023                | 0.8816         | 0.0303            | 0.5969                | 0.0           | 0.5237                  | 0.0023           | 0.8715    | 0.0301       | 0.5968           |
| 0.2637        | 3.0   | 24435 | 0.3531          | 0.3717   | 0.4480        | 0.6171           | nan                | 0.5638                       | 0.0409                | 0.8804         | 0.1563            | 0.5984                | 0.0           | 0.5633                  | 0.0409           | 0.8723    | 0.1554       | 0.5982           |
| 0.4715        | 4.0   | 32580 | 0.3337          | 0.3653   | 0.4398        | 0.6073           | nan                | 0.6172                       | 0.1068                | 0.8077         | 0.0976            | 0.5698                | 0.0           | 0.6164                  | 0.1068           | 0.8015    | 0.0976       | 0.5696           |
| 0.0668        | 5.0   | 40725 | 0.3305          | 0.4242   | 0.5107        | 0.6733           | nan                | 0.6872                       | 0.1915                | 0.8549         | 0.1797            | 0.6404                | 0.0           | 0.6865                  | 0.1914           | 0.8475    | 0.1795       | 0.6401           |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2