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segformer-b0-practice-7-11

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  1. README.md +152 -196
  2. config.json +86 -0
  3. model.safetensors +3 -0
  4. runs/Jul11_17-57-26_ae9983dcd873/events.out.tfevents.1720720649.ae9983dcd873.404.0 +3 -0
  5. training_args.bin +3 -0
README.md CHANGED
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Uses
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- ### Direct Use
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- ## Bias, Risks, and Limitations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Factors
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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  ---
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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: segformer-b0-practice-7-11
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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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+
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+ # segformer-b0-practice-7-11
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the chugz/SEM dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1941
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+ - Mean Iou: 0.5755
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+ - Mean Accuracy: 0.7536
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+ - Overall Accuracy: 0.9340
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+ - Accuracy Background: nan
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+ - Accuracy Silver: 0.9537
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+ - Accuracy Glass: 0.0749
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+ - Accuracy Silicon: 0.9909
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+ - Accuracy Void: 0.8095
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+ - Accuracy Interfacial void: 0.9391
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+ - Iou Background: 0.0
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+ - Iou Silver: 0.8956
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+ - Iou Glass: 0.0717
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+ - Iou Silicon: 0.9803
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+ - Iou Void: 0.6885
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+ - Iou Interfacial void: 0.8168
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Silver | Accuracy Glass | Accuracy Silicon | Accuracy Void | Accuracy Interfacial void | Iou Background | Iou Silver | Iou Glass | Iou Silicon | Iou Void | Iou Interfacial void |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:---------------:|:--------------:|:----------------:|:-------------:|:-------------------------:|:--------------:|:----------:|:---------:|:-----------:|:--------:|:--------------------:|
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+ | 1.2415 | 0.6061 | 20 | 1.3040 | 0.2800 | 0.4306 | 0.7396 | nan | 0.9612 | 0.0069 | 0.9153 | 0.0 | 0.2698 | 0.0 | 0.5882 | 0.0069 | 0.8301 | 0.0 | 0.2549 |
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+ | 0.9185 | 1.2121 | 40 | 0.7957 | 0.3642 | 0.5203 | 0.8249 | nan | 0.9524 | 0.0 | 0.9539 | 0.0000 | 0.6951 | 0.0 | 0.7181 | 0.0 | 0.8980 | 0.0000 | 0.5691 |
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+ | 0.6451 | 1.8182 | 60 | 0.6017 | 0.3679 | 0.5206 | 0.8131 | nan | 0.8835 | 0.0 | 0.9456 | 0.0049 | 0.7688 | 0.0 | 0.6888 | 0.0 | 0.9053 | 0.0049 | 0.6086 |
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+ | 0.5145 | 2.4242 | 80 | 0.5779 | 0.4033 | 0.5635 | 0.8393 | nan | 0.9514 | 0.0 | 0.9371 | 0.1596 | 0.7693 | 0.0 | 0.7321 | 0.0 | 0.9199 | 0.1516 | 0.6165 |
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+ | 0.9509 | 3.0303 | 100 | 0.5006 | 0.4017 | 0.5540 | 0.8420 | nan | 0.9510 | 0.0 | 0.9548 | 0.1047 | 0.7592 | 0.0 | 0.7312 | 0.0 | 0.9273 | 0.1010 | 0.6506 |
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+ | 0.6075 | 3.6364 | 120 | 0.4274 | 0.4688 | 0.6372 | 0.8776 | nan | 0.9414 | 0.0 | 0.9613 | 0.4300 | 0.8535 | 0.0 | 0.7953 | 0.0 | 0.9395 | 0.3709 | 0.7070 |
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+ | 0.4155 | 4.2424 | 140 | 0.3949 | 0.4994 | 0.6781 | 0.8912 | nan | 0.9260 | 0.0 | 0.9560 | 0.5908 | 0.9177 | 0.0 | 0.8258 | 0.0 | 0.9412 | 0.4981 | 0.7313 |
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+ | 0.4072 | 4.8485 | 160 | 0.3538 | 0.5040 | 0.6794 | 0.8961 | nan | 0.9463 | 0.0 | 0.9668 | 0.6083 | 0.8758 | 0.0 | 0.8231 | 0.0 | 0.9540 | 0.4978 | 0.7493 |
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+ | 0.3085 | 5.4545 | 180 | 0.3441 | 0.5033 | 0.6814 | 0.8919 | nan | 0.9506 | 0.0 | 0.9536 | 0.6344 | 0.8684 | 0.0 | 0.8170 | 0.0 | 0.9417 | 0.5214 | 0.7398 |
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+ | 0.2885 | 6.0606 | 200 | 0.3357 | 0.5136 | 0.6967 | 0.8989 | nan | 0.9082 | 0.0 | 0.9780 | 0.6987 | 0.8987 | 0.0 | 0.8389 | 0.0 | 0.9476 | 0.5652 | 0.7302 |
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+ | 0.2754 | 6.6667 | 220 | 0.3052 | 0.5253 | 0.7061 | 0.9094 | nan | 0.9355 | 0.0 | 0.9714 | 0.6918 | 0.9319 | 0.0 | 0.8553 | 0.0 | 0.9577 | 0.5742 | 0.7643 |
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+ | 0.2942 | 7.2727 | 240 | 0.2893 | 0.5226 | 0.6948 | 0.9087 | nan | 0.9388 | 0.0 | 0.9805 | 0.6373 | 0.9175 | 0.0 | 0.8466 | 0.0 | 0.9618 | 0.5504 | 0.7766 |
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+ | 0.2324 | 7.8788 | 260 | 0.3018 | 0.5221 | 0.7053 | 0.9053 | nan | 0.9031 | 0.0 | 0.9754 | 0.6931 | 0.9549 | 0.0 | 0.8499 | 0.0 | 0.9659 | 0.5796 | 0.7370 |
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+ | 0.2872 | 8.4848 | 280 | 0.2758 | 0.5339 | 0.7129 | 0.9139 | nan | 0.9160 | 0.0 | 0.9880 | 0.7207 | 0.9396 | 0.0 | 0.8585 | 0.0 | 0.9739 | 0.6018 | 0.7694 |
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+ | 0.2076 | 9.0909 | 300 | 0.2615 | 0.5317 | 0.7011 | 0.9168 | nan | 0.9431 | 0.0 | 0.9920 | 0.6451 | 0.9253 | 0.0 | 0.8670 | 0.0 | 0.9715 | 0.5706 | 0.7811 |
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+ | 0.2796 | 9.6970 | 320 | 0.2718 | 0.5226 | 0.6956 | 0.9078 | nan | 0.9573 | 0.0 | 0.9720 | 0.6536 | 0.8953 | 0.0 | 0.8501 | 0.0 | 0.9577 | 0.5505 | 0.7771 |
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+ | 0.3215 | 10.3030 | 340 | 0.2627 | 0.5414 | 0.7207 | 0.9193 | nan | 0.9303 | 0.0 | 0.9847 | 0.7407 | 0.9476 | 0.0 | 0.8680 | 0.0 | 0.9735 | 0.6193 | 0.7878 |
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+ | 0.209 | 10.9091 | 360 | 0.2516 | 0.5337 | 0.7073 | 0.9189 | nan | 0.9487 | 0.0 | 0.9898 | 0.6761 | 0.9221 | 0.0 | 0.8733 | 0.0 | 0.9734 | 0.5741 | 0.7818 |
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+ | 0.2928 | 11.5152 | 380 | 0.2606 | 0.5457 | 0.7354 | 0.9225 | nan | 0.9284 | 0.0 | 0.9831 | 0.8203 | 0.9452 | 0.0 | 0.8774 | 0.0 | 0.9733 | 0.6401 | 0.7831 |
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+ | 0.2246 | 12.1212 | 400 | 0.2519 | 0.5378 | 0.7107 | 0.9180 | nan | 0.9373 | 0.0 | 0.9906 | 0.6993 | 0.9263 | 0.0 | 0.8686 | 0.0 | 0.9691 | 0.6136 | 0.7755 |
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+ | 0.2386 | 12.7273 | 420 | 0.2477 | 0.5443 | 0.7290 | 0.9212 | nan | 0.9407 | 0.0001 | 0.9828 | 0.7968 | 0.9245 | 0.0 | 0.8733 | 0.0001 | 0.9703 | 0.6294 | 0.7929 |
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+ | 0.1734 | 13.3333 | 440 | 0.2285 | 0.5466 | 0.7234 | 0.9234 | nan | 0.9479 | 0.0005 | 0.9879 | 0.7531 | 0.9274 | 0.0 | 0.8791 | 0.0005 | 0.9747 | 0.6310 | 0.7940 |
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+ | 0.1809 | 13.9394 | 460 | 0.2304 | 0.5501 | 0.7254 | 0.9259 | nan | 0.9451 | 0.0002 | 0.9923 | 0.7544 | 0.9349 | 0.0 | 0.8799 | 0.0002 | 0.9774 | 0.6386 | 0.8047 |
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+ | 0.2162 | 14.5455 | 480 | 0.2431 | 0.5514 | 0.7364 | 0.9257 | nan | 0.9451 | 0.0022 | 0.9792 | 0.8061 | 0.9493 | 0.0 | 0.8825 | 0.0022 | 0.9714 | 0.6526 | 0.7996 |
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+ | 0.1973 | 15.1515 | 500 | 0.2519 | 0.5480 | 0.7321 | 0.9188 | nan | 0.9306 | 0.0002 | 0.9773 | 0.8158 | 0.9364 | 0.0 | 0.8645 | 0.0002 | 0.9631 | 0.6636 | 0.7967 |
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+ | 0.1218 | 15.7576 | 520 | 0.2275 | 0.5513 | 0.7291 | 0.9245 | nan | 0.9463 | 0.0027 | 0.9827 | 0.7704 | 0.9436 | 0.0 | 0.8842 | 0.0027 | 0.9712 | 0.6524 | 0.7972 |
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+ | 0.1809 | 16.3636 | 540 | 0.2323 | 0.5540 | 0.7339 | 0.9271 | nan | 0.9406 | 0.0014 | 0.9876 | 0.7905 | 0.9495 | 0.0 | 0.8827 | 0.0014 | 0.9744 | 0.6583 | 0.8071 |
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+ | 0.1484 | 16.9697 | 560 | 0.2364 | 0.5527 | 0.7374 | 0.9223 | nan | 0.9343 | 0.0027 | 0.9759 | 0.8231 | 0.9509 | 0.0 | 0.8694 | 0.0027 | 0.9657 | 0.6680 | 0.8105 |
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+ | 0.1464 | 17.5758 | 580 | 0.2338 | 0.5519 | 0.7376 | 0.9254 | nan | 0.9397 | 0.0030 | 0.9849 | 0.8248 | 0.9354 | 0.0 | 0.8782 | 0.0030 | 0.9752 | 0.6523 | 0.8029 |
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+ | 0.1389 | 18.1818 | 600 | 0.2355 | 0.5541 | 0.7350 | 0.9227 | nan | 0.9281 | 0.0044 | 0.9822 | 0.8067 | 0.9537 | 0.0 | 0.8759 | 0.0044 | 0.9722 | 0.6756 | 0.7967 |
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+ | 0.115 | 18.7879 | 620 | 0.2175 | 0.5478 | 0.7175 | 0.9242 | nan | 0.9554 | 0.0080 | 0.9932 | 0.7125 | 0.9180 | 0.0 | 0.8806 | 0.0079 | 0.9735 | 0.6302 | 0.7947 |
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+ | 0.1704 | 19.3939 | 640 | 0.2246 | 0.5552 | 0.7320 | 0.9283 | nan | 0.9413 | 0.0079 | 0.9919 | 0.7671 | 0.9517 | 0.0 | 0.8883 | 0.0078 | 0.9815 | 0.6585 | 0.7951 |
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+ | 0.1537 | 20.0 | 660 | 0.2222 | 0.5590 | 0.7370 | 0.9299 | nan | 0.9461 | 0.0193 | 0.9919 | 0.7830 | 0.9445 | 0.0 | 0.8923 | 0.0189 | 0.9803 | 0.6620 | 0.8002 |
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+ | 0.1605 | 20.6061 | 680 | 0.2174 | 0.5578 | 0.7417 | 0.9273 | nan | 0.9388 | 0.0084 | 0.9865 | 0.8359 | 0.9390 | 0.0 | 0.8833 | 0.0083 | 0.9749 | 0.6706 | 0.8096 |
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+ | 0.1237 | 21.2121 | 700 | 0.2154 | 0.5639 | 0.7384 | 0.9312 | nan | 0.9496 | 0.0219 | 0.9935 | 0.7867 | 0.9404 | 0.0 | 0.8913 | 0.0214 | 0.9812 | 0.6799 | 0.8096 |
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+ | 0.1288 | 21.8182 | 720 | 0.2214 | 0.5627 | 0.7430 | 0.9303 | nan | 0.9406 | 0.0237 | 0.9907 | 0.8101 | 0.9497 | 0.0 | 0.8926 | 0.0233 | 0.9799 | 0.6771 | 0.8034 |
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+ | 0.122 | 22.4242 | 740 | 0.2160 | 0.5675 | 0.7469 | 0.9309 | nan | 0.9367 | 0.0376 | 0.9922 | 0.8161 | 0.9519 | 0.0 | 0.8921 | 0.0361 | 0.9816 | 0.6884 | 0.8067 |
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+ | 0.1608 | 23.0303 | 760 | 0.2112 | 0.5613 | 0.7412 | 0.9304 | nan | 0.9417 | 0.0160 | 0.9934 | 0.8137 | 0.9410 | 0.0 | 0.8907 | 0.0157 | 0.9802 | 0.6695 | 0.8116 |
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+ | 0.1258 | 23.6364 | 780 | 0.2230 | 0.5611 | 0.7425 | 0.9293 | nan | 0.9367 | 0.0196 | 0.9898 | 0.8148 | 0.9518 | 0.0 | 0.8894 | 0.0192 | 0.9808 | 0.6733 | 0.8036 |
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+ | 0.1333 | 24.2424 | 800 | 0.2171 | 0.5606 | 0.7377 | 0.9280 | nan | 0.9420 | 0.0207 | 0.9871 | 0.7868 | 0.9520 | 0.0 | 0.8865 | 0.0203 | 0.9773 | 0.6734 | 0.8063 |
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+ | 0.1562 | 24.8485 | 820 | 0.2183 | 0.5644 | 0.7477 | 0.9302 | nan | 0.9301 | 0.0299 | 0.9929 | 0.8333 | 0.9523 | 0.0 | 0.8882 | 0.0291 | 0.9813 | 0.6798 | 0.8081 |
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+ | 0.1039 | 25.4545 | 840 | 0.2106 | 0.5648 | 0.7389 | 0.9302 | nan | 0.9506 | 0.0412 | 0.9920 | 0.7729 | 0.9379 | 0.0 | 0.8890 | 0.0392 | 0.9804 | 0.6668 | 0.8136 |
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+ | 0.1079 | 26.0606 | 860 | 0.2099 | 0.5625 | 0.7449 | 0.9299 | nan | 0.9385 | 0.0233 | 0.9896 | 0.8246 | 0.9486 | 0.0 | 0.8917 | 0.0227 | 0.9796 | 0.6752 | 0.8056 |
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+ | 0.0995 | 26.6667 | 880 | 0.2122 | 0.5620 | 0.7403 | 0.9310 | nan | 0.9464 | 0.0210 | 0.9913 | 0.7954 | 0.9474 | 0.0 | 0.8916 | 0.0207 | 0.9793 | 0.6690 | 0.8115 |
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+ | 0.1326 | 27.2727 | 900 | 0.2150 | 0.5611 | 0.7428 | 0.9291 | nan | 0.9412 | 0.0198 | 0.9879 | 0.8193 | 0.9459 | 0.0 | 0.8868 | 0.0195 | 0.9785 | 0.6684 | 0.8130 |
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+ | 0.1602 | 27.8788 | 920 | 0.2108 | 0.5645 | 0.7438 | 0.9312 | nan | 0.9388 | 0.0249 | 0.9925 | 0.8091 | 0.9537 | 0.0 | 0.8935 | 0.0243 | 0.9812 | 0.6835 | 0.8044 |
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+ | 0.1758 | 28.4848 | 940 | 0.2116 | 0.5682 | 0.7461 | 0.9326 | nan | 0.9520 | 0.0347 | 0.9891 | 0.8089 | 0.9459 | 0.0 | 0.9004 | 0.0336 | 0.9775 | 0.6888 | 0.8089 |
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+ | 0.3986 | 29.0909 | 960 | 0.1990 | 0.5783 | 0.7581 | 0.9314 | nan | 0.9414 | 0.1021 | 0.9878 | 0.8095 | 0.9496 | 0.0 | 0.8923 | 0.0941 | 0.9785 | 0.6951 | 0.8099 |
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+ | 0.1477 | 29.6970 | 980 | 0.2202 | 0.5627 | 0.7442 | 0.9290 | nan | 0.9450 | 0.0213 | 0.9847 | 0.8258 | 0.9442 | 0.0 | 0.8857 | 0.0210 | 0.9749 | 0.6807 | 0.8142 |
115
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+
149
+
150
+ ### Framework versions
151
+
152
+ - Transformers 4.42.4
153
+ - Pytorch 2.3.0+cu121
154
+ - Datasets 2.20.0
155
+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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