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--- |
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library_name: transformers |
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license: other |
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base_model: nvidia/mit-b0 |
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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: custom-object-test6 |
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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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# custom-object-test6 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sungile/custom-object-masking5 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3457 |
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- Mean Iou: 0.3368 |
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- Mean Accuracy: 0.6736 |
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- Overall Accuracy: 0.6736 |
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- Accuracy Unknown: nan |
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- Accuracy Background: 0.6736 |
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- Accuracy Object: nan |
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- Iou Unknown: 0.0 |
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- Iou Background: 0.6736 |
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- Iou Object: nan |
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unknown | Accuracy Background | Accuracy Object | Iou Unknown | Iou Background | Iou Object | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------------:|:---------------:|:-----------:|:--------------:|:----------:| |
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| 0.7667 | 0.25 | 20 | 1.0272 | 0.3026 | 0.9079 | 0.9079 | nan | 0.9079 | nan | 0.0 | 0.9079 | 0.0 | |
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| 0.8127 | 0.5 | 40 | 0.8213 | 0.2803 | 0.8409 | 0.8409 | nan | 0.8409 | nan | 0.0 | 0.8409 | 0.0 | |
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| 0.5588 | 0.75 | 60 | 0.7310 | 0.4304 | 0.8608 | 0.8608 | nan | 0.8608 | nan | 0.0 | 0.8608 | nan | |
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| 0.6156 | 1.0 | 80 | 0.5317 | 0.3130 | 0.6261 | 0.6261 | nan | 0.6261 | nan | 0.0 | 0.6261 | nan | |
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| 0.5077 | 1.25 | 100 | 0.4617 | 0.3482 | 0.6964 | 0.6964 | nan | 0.6964 | nan | 0.0 | 0.6964 | nan | |
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| 0.5612 | 1.5 | 120 | 0.4336 | 0.3341 | 0.6683 | 0.6683 | nan | 0.6683 | nan | 0.0 | 0.6683 | nan | |
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| 0.4468 | 1.75 | 140 | 0.3946 | 0.3442 | 0.6883 | 0.6883 | nan | 0.6883 | nan | 0.0 | 0.6883 | nan | |
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| 0.292 | 2.0 | 160 | 0.3554 | 0.3041 | 0.6081 | 0.6081 | nan | 0.6081 | nan | 0.0 | 0.6081 | nan | |
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| 0.3769 | 2.25 | 180 | 0.3798 | 0.3402 | 0.6805 | 0.6805 | nan | 0.6805 | nan | 0.0 | 0.6805 | nan | |
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| 0.3386 | 2.5 | 200 | 0.3493 | 0.3147 | 0.6293 | 0.6293 | nan | 0.6293 | nan | 0.0 | 0.6293 | nan | |
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| 0.2689 | 2.75 | 220 | 0.3736 | 0.3492 | 0.6984 | 0.6984 | nan | 0.6984 | nan | 0.0 | 0.6984 | nan | |
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| 0.2539 | 3.0 | 240 | 0.3457 | 0.3368 | 0.6736 | 0.6736 | nan | 0.6736 | nan | 0.0 | 0.6736 | nan | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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