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--- |
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license: other |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: segformer-b0-finetuned-segments-toolwear |
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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-toolwear |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on the HorcruxNo13/toolwear_segmentsai dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2940 |
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- Mean Iou: 0.4104 |
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- Mean Accuracy: 0.8207 |
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- Overall Accuracy: 0.8207 |
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- Accuracy Unlabeled: nan |
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- Accuracy Tool: nan |
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- Accuracy Wear: 0.8207 |
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- Iou Unlabeled: 0.0 |
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- Iou Tool: nan |
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- Iou Wear: 0.8207 |
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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: 25 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Tool | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:| |
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| 0.843 | 1.82 | 20 | 0.8832 | 0.4637 | 0.9274 | 0.9274 | nan | nan | 0.9274 | 0.0 | nan | 0.9274 | |
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| 0.6849 | 3.64 | 40 | 0.5914 | 0.4361 | 0.8722 | 0.8722 | nan | nan | 0.8722 | 0.0 | nan | 0.8722 | |
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| 0.5085 | 5.45 | 60 | 0.5178 | 0.4628 | 0.9256 | 0.9256 | nan | nan | 0.9256 | 0.0 | nan | 0.9256 | |
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| 0.4027 | 7.27 | 80 | 0.5099 | 0.4598 | 0.9195 | 0.9195 | nan | nan | 0.9195 | 0.0 | nan | 0.9195 | |
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| 0.3536 | 9.09 | 100 | 0.4262 | 0.4365 | 0.8730 | 0.8730 | nan | nan | 0.8730 | 0.0 | nan | 0.8730 | |
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| 0.3657 | 10.91 | 120 | 0.3891 | 0.4228 | 0.8457 | 0.8457 | nan | nan | 0.8457 | 0.0 | nan | 0.8457 | |
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| 0.3234 | 12.73 | 140 | 0.4221 | 0.4377 | 0.8754 | 0.8754 | nan | nan | 0.8754 | 0.0 | nan | 0.8754 | |
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| 0.2874 | 14.55 | 160 | 0.3355 | 0.4098 | 0.8197 | 0.8197 | nan | nan | 0.8197 | 0.0 | nan | 0.8197 | |
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| 0.2335 | 16.36 | 180 | 0.3570 | 0.4266 | 0.8531 | 0.8531 | nan | nan | 0.8531 | 0.0 | nan | 0.8531 | |
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| 0.2167 | 18.18 | 200 | 0.3238 | 0.4404 | 0.8808 | 0.8808 | nan | nan | 0.8808 | 0.0 | nan | 0.8808 | |
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| 0.2201 | 20.0 | 220 | 0.3103 | 0.4185 | 0.8370 | 0.8370 | nan | nan | 0.8370 | 0.0 | nan | 0.8370 | |
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| 0.205 | 21.82 | 240 | 0.2881 | 0.4115 | 0.8230 | 0.8230 | nan | nan | 0.8230 | 0.0 | nan | 0.8230 | |
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| 0.241 | 23.64 | 260 | 0.2940 | 0.4104 | 0.8207 | 0.8207 | nan | nan | 0.8207 | 0.0 | nan | 0.8207 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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