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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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- gear-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: segformer-b0-finetuned-segments-gear2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# segformer-b0-finetuned-segments-gear2 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on the marcomameli01/gear dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1268 |
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- Mean Iou: 0.1254 |
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- Mean Accuracy: 0.2509 |
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- Overall Accuracy: 0.2509 |
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- Per Category Iou: [0.0, 0.2508641975308642] |
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- Per Category Accuracy: [nan, 0.2508641975308642] |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------:|:--------------------------:| |
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| 0.4614 | 5.0 | 20 | 0.4427 | 0.0741 | 0.1481 | 0.1481 | [0.0, 0.14814814814814814] | [nan, 0.14814814814814814] | |
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| 0.3327 | 10.0 | 40 | 0.2933 | 0.1726 | 0.3453 | 0.3453 | [0.0, 0.34528395061728395] | [nan, 0.34528395061728395] | |
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| 0.2305 | 15.0 | 60 | 0.2244 | 0.0382 | 0.0763 | 0.0763 | [0.0, 0.07634567901234568] | [nan, 0.07634567901234568] | |
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| 0.2011 | 20.0 | 80 | 0.2130 | 0.0374 | 0.0748 | 0.0748 | [0.0, 0.07476543209876543] | [nan, 0.07476543209876543] | |
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| 0.1846 | 25.0 | 100 | 0.1672 | 0.1037 | 0.2073 | 0.2073 | [0.0, 0.20730864197530866] | [nan, 0.20730864197530866] | |
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| 0.1622 | 30.0 | 120 | 0.1532 | 0.0805 | 0.1611 | 0.1611 | [0.0, 0.1610864197530864] | [nan, 0.1610864197530864] | |
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| 0.139 | 35.0 | 140 | 0.1396 | 0.0971 | 0.1942 | 0.1942 | [0.0, 0.19417283950617284] | [nan, 0.19417283950617284] | |
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| 0.1342 | 40.0 | 160 | 0.1283 | 0.0748 | 0.1496 | 0.1496 | [0.0, 0.14962962962962964] | [nan, 0.14962962962962964] | |
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| 0.128 | 45.0 | 180 | 0.1224 | 0.1128 | 0.2256 | 0.2256 | [0.0, 0.22558024691358025] | [nan, 0.22558024691358025] | |
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| 0.1243 | 50.0 | 200 | 0.1268 | 0.1254 | 0.2509 | 0.2509 | [0.0, 0.2508641975308642] | [nan, 0.2508641975308642] | |
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### Framework versions |
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- Transformers 4.20.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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