readme: ---
  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
  