readme: ---
  license: apache-2.0
  tags:
  - vision
  - gear-segmentation
  - generated_from_trainer
  model-index:
  - name: segformer-b0-finetuned-segments-gear2
    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-segments-gear2
  
  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the marcomameli01/gear dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1268
  - Mean Iou: 0.1254
  - Mean Accuracy: 0.2509
  - Overall Accuracy: 0.2509
  - Per Category Iou: [0.0, 0.2508641975308642]
  - Per Category Accuracy: [nan, 0.2508641975308642]
  
  ## 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: 50
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou           | Per Category Accuracy      |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------:|:--------------------------:|
  | 0.4614        | 5.0   | 20   | 0.4427          | 0.0741   | 0.1481        | 0.1481           | [0.0, 0.14814814814814814] | [nan, 0.14814814814814814] |
  | 0.3327        | 10.0  | 40   | 0.2933          | 0.1726   | 0.3453        | 0.3453           | [0.0, 0.34528395061728395] | [nan, 0.34528395061728395] |
  | 0.2305        | 15.0  | 60   | 0.2244          | 0.0382   | 0.0763        | 0.0763           | [0.0, 0.07634567901234568] | [nan, 0.07634567901234568] |
  | 0.2011        | 20.0  | 80   | 0.2130          | 0.0374   | 0.0748        | 0.0748           | [0.0, 0.07476543209876543] | [nan, 0.07476543209876543] |
  | 0.1846        | 25.0  | 100  | 0.1672          | 0.1037   | 0.2073        | 0.2073           | [0.0, 0.20730864197530866] | [nan, 0.20730864197530866] |
  | 0.1622        | 30.0  | 120  | 0.1532          | 0.0805   | 0.1611        | 0.1611           | [0.0, 0.1610864197530864]  | [nan, 0.1610864197530864]  |
  | 0.139         | 35.0  | 140  | 0.1396          | 0.0971   | 0.1942        | 0.1942           | [0.0, 0.19417283950617284] | [nan, 0.19417283950617284] |
  | 0.1342        | 40.0  | 160  | 0.1283          | 0.0748   | 0.1496        | 0.1496           | [0.0, 0.14962962962962964] | [nan, 0.14962962962962964] |
  | 0.128         | 45.0  | 180  | 0.1224          | 0.1128   | 0.2256        | 0.2256           | [0.0, 0.22558024691358025] | [nan, 0.22558024691358025] |
  | 0.1243        | 50.0  | 200  | 0.1268          | 0.1254   | 0.2509        | 0.2509           | [0.0, 0.2508641975308642]  | [nan, 0.2508641975308642]  |
  
  
  ### Framework versions
  
  - Transformers 4.20.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  