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saad7489
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segformer-b0-finetuned-segments-SixrayKnife8-19-2024

Image Segmentation
TensorBoard
Safetensors
segformer
vision
Generated from Trainer
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End of training

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  1. README.md +16 -16
  2. model.safetensors +1 -1
  3. runs/Aug20_08-35-16_d98ed18656c3/events.out.tfevents.1724142928.d98ed18656c3.1590.0 +3 -0
  4. runs/Aug20_08-37-53_d98ed18656c3/events.out.tfevents.1724143089.d98ed18656c3.1590.1 +3 -0
  5. runs/Aug20_08-43-43_d98ed18656c3/events.out.tfevents.1724143434.d98ed18656c3.1590.2 +3 -0
  6. runs/Aug20_08-56-13_d98ed18656c3/events.out.tfevents.1724144182.d98ed18656c3.1590.3 +3 -0
  7. training_args.bin +1 -1
README.md CHANGED
@@ -1,6 +1,6 @@
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  ---
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- base_model: nvidia/mit-b0
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  license: other
 
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  tags:
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  - vision
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  - image-segmentation
@@ -17,16 +17,16 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixraygunTest dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8755
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- - Mean Iou: 0.5092
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- - Mean Accuracy: 0.8594
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- - Overall Accuracy: 0.8549
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  - Accuracy Object1: nan
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- - Accuracy Object2: 0.7708
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- - Accuracy Object3: 0.9479
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  - Iou Object1: 0.0
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- - Iou Object2: 0.7432
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- - Iou Object3: 0.7844
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  ## Model description
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@@ -57,13 +57,13 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Object1 | Accuracy Object2 | Accuracy Object3 | Iou Object1 | Iou Object2 | Iou Object3 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:----------------:|:----------------:|:-----------:|:-----------:|:-----------:|
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- | 1.0985 | 1.4286 | 20 | 1.0886 | 0.2471 | 0.5995 | 0.5802 | nan | 0.2163 | 0.9827 | 0.0 | 0.2130 | 0.5284 |
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- | 1.051 | 2.8571 | 40 | 1.0140 | 0.3757 | 0.7369 | 0.7259 | nan | 0.5196 | 0.9542 | 0.0 | 0.4990 | 0.6280 |
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- | 1.0047 | 4.2857 | 60 | 0.9555 | 0.4411 | 0.8033 | 0.7952 | nan | 0.6423 | 0.9643 | 0.0 | 0.6235 | 0.6997 |
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- | 0.9207 | 5.7143 | 80 | 0.9049 | 0.4783 | 0.8356 | 0.8295 | nan | 0.7139 | 0.9574 | 0.0 | 0.6907 | 0.7441 |
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- | 0.9117 | 7.1429 | 100 | 0.8798 | 0.4988 | 0.8516 | 0.8466 | nan | 0.7523 | 0.9508 | 0.0 | 0.7257 | 0.7707 |
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- | 0.8705 | 8.5714 | 120 | 0.8692 | 0.5038 | 0.8543 | 0.8496 | nan | 0.7608 | 0.9478 | 0.0 | 0.7332 | 0.7783 |
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- | 0.8738 | 10.0 | 140 | 0.8755 | 0.5092 | 0.8594 | 0.8549 | nan | 0.7708 | 0.9479 | 0.0 | 0.7432 | 0.7844 |
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  ### Framework versions
 
1
  ---
 
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  license: other
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+ base_model: nvidia/mit-b0
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  tags:
5
  - vision
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  - image-segmentation
 
17
 
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixraygunTest dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7621
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+ - Mean Iou: 0.4717
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+ - Mean Accuracy: 0.8197
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+ - Overall Accuracy: 0.8114
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  - Accuracy Object1: nan
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+ - Accuracy Object2: 0.6867
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+ - Accuracy Object3: 0.9527
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  - Iou Object1: 0.0
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+ - Iou Object2: 0.6649
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+ - Iou Object3: 0.7502
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Object1 | Accuracy Object2 | Accuracy Object3 | Iou Object1 | Iou Object2 | Iou Object3 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:----------------:|:----------------:|:-----------:|:-----------:|:-----------:|
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+ | 0.9327 | 1.4286 | 20 | 1.0058 | 0.3119 | 0.6386 | 0.6235 | nan | 0.3968 | 0.8803 | 0.0 | 0.3621 | 0.5736 |
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+ | 0.9028 | 2.8571 | 40 | 0.8719 | 0.3792 | 0.7295 | 0.7148 | nan | 0.4933 | 0.9656 | 0.0 | 0.4796 | 0.6580 |
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+ | 0.8514 | 4.2857 | 60 | 0.8211 | 0.4199 | 0.7734 | 0.7615 | nan | 0.5833 | 0.9635 | 0.0 | 0.5662 | 0.6936 |
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+ | 0.8251 | 5.7143 | 80 | 0.7680 | 0.4585 | 0.8077 | 0.7986 | nan | 0.6613 | 0.9541 | 0.0 | 0.6389 | 0.7364 |
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+ | 0.8048 | 7.1429 | 100 | 0.7591 | 0.4643 | 0.8141 | 0.8053 | nan | 0.6717 | 0.9565 | 0.0 | 0.6514 | 0.7414 |
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+ | 0.7667 | 8.5714 | 120 | 0.7591 | 0.4751 | 0.8230 | 0.8153 | nan | 0.6997 | 0.9462 | 0.0 | 0.6737 | 0.7517 |
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+ | 0.7748 | 10.0 | 140 | 0.7621 | 0.4717 | 0.8197 | 0.8114 | nan | 0.6867 | 0.9527 | 0.0 | 0.6649 | 0.7502 |
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  ### Framework versions
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