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segformer-b0-finetuned-lipid-droplets-v2

Image Segmentation
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segformer
vision
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  ---
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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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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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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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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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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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- #### 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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- #### 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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+ 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-finetuned-lipid-droplets-v2
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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-finetuned-lipid-droplets-v2
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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 jhaberbe/lipid-droplets-v3 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0136
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+ - Mean Iou: 0.4619
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+ - Mean Accuracy: 0.9238
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+ - Overall Accuracy: 0.9238
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Lipid: 0.9238
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+ - Iou Unlabeled: 0.0
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+ - Iou Lipid: 0.9238
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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: 500
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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 Unlabeled | Accuracy Lipid | Iou Unlabeled | Iou Lipid |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
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+ | 0.5743 | 2.22 | 20 | 0.6276 | 0.2015 | 0.4030 | 0.4030 | nan | 0.4030 | 0.0 | 0.4030 |
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+ | 0.4173 | 4.44 | 40 | 0.5383 | 0.3448 | 0.6896 | 0.6896 | nan | 0.6896 | 0.0 | 0.6896 |
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+ | 0.333 | 6.67 | 60 | 0.4480 | 0.4088 | 0.8177 | 0.8177 | nan | 0.8177 | 0.0 | 0.8177 |
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+ | 0.2819 | 8.89 | 80 | 0.3045 | 0.3712 | 0.7424 | 0.7424 | nan | 0.7424 | 0.0 | 0.7424 |
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+ | 0.2194 | 11.11 | 100 | 0.3314 | 0.4222 | 0.8443 | 0.8443 | nan | 0.8443 | 0.0 | 0.8443 |
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+ | 0.1676 | 13.33 | 120 | 0.2670 | 0.3984 | 0.7968 | 0.7968 | nan | 0.7968 | 0.0 | 0.7968 |
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+ | 0.1567 | 15.56 | 140 | 0.2553 | 0.2902 | 0.5804 | 0.5804 | nan | 0.5804 | 0.0 | 0.5804 |
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+ | 0.1211 | 17.78 | 160 | 0.2725 | 0.4144 | 0.8287 | 0.8287 | nan | 0.8287 | 0.0 | 0.8287 |
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+ | 0.1616 | 20.0 | 180 | 0.1689 | 0.3260 | 0.6521 | 0.6521 | nan | 0.6521 | 0.0 | 0.6521 |
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+ | 0.0906 | 22.22 | 200 | 0.1749 | 0.3633 | 0.7265 | 0.7265 | nan | 0.7265 | 0.0 | 0.7265 |
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+ | 0.0999 | 24.44 | 220 | 0.1396 | 0.3785 | 0.7569 | 0.7569 | nan | 0.7569 | 0.0 | 0.7569 |
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+ | 0.0867 | 26.67 | 240 | 0.2055 | 0.4396 | 0.8791 | 0.8791 | nan | 0.8791 | 0.0 | 0.8791 |
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+ | 0.0763 | 28.89 | 260 | 0.1603 | 0.4037 | 0.8073 | 0.8073 | nan | 0.8073 | 0.0 | 0.8073 |
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+ | 0.0897 | 31.11 | 280 | 0.1673 | 0.4128 | 0.8255 | 0.8255 | nan | 0.8255 | 0.0 | 0.8255 |
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+ | 0.0741 | 33.33 | 300 | 0.1626 | 0.4092 | 0.8184 | 0.8184 | nan | 0.8184 | 0.0 | 0.8184 |
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+ | 0.0626 | 35.56 | 320 | 0.1438 | 0.4162 | 0.8324 | 0.8324 | nan | 0.8324 | 0.0 | 0.8324 |
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+ | 0.0483 | 37.78 | 340 | 0.1334 | 0.4162 | 0.8323 | 0.8323 | nan | 0.8323 | 0.0 | 0.8323 |
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+ | 0.0905 | 40.0 | 360 | 0.1267 | 0.4234 | 0.8468 | 0.8468 | nan | 0.8468 | 0.0 | 0.8468 |
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+ | 0.0573 | 42.22 | 380 | 0.0694 | 0.3928 | 0.7856 | 0.7856 | nan | 0.7856 | 0.0 | 0.7856 |
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+ | 0.0422 | 44.44 | 400 | 0.1001 | 0.4276 | 0.8552 | 0.8552 | nan | 0.8552 | 0.0 | 0.8552 |
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+ | 0.0523 | 46.67 | 420 | 0.1224 | 0.4323 | 0.8647 | 0.8647 | nan | 0.8647 | 0.0 | 0.8647 |
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+ | 0.0399 | 48.89 | 440 | 0.1203 | 0.4430 | 0.8860 | 0.8860 | nan | 0.8860 | 0.0 | 0.8860 |
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+ | 0.0398 | 51.11 | 460 | 0.0790 | 0.4073 | 0.8146 | 0.8146 | nan | 0.8146 | 0.0 | 0.8146 |
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+ | 0.0406 | 53.33 | 480 | 0.1032 | 0.4511 | 0.9022 | 0.9022 | nan | 0.9022 | 0.0 | 0.9022 |
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+ | 0.0443 | 55.56 | 500 | 0.0835 | 0.4246 | 0.8492 | 0.8492 | nan | 0.8492 | 0.0 | 0.8492 |
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+ | 0.0516 | 57.78 | 520 | 0.1175 | 0.4414 | 0.8828 | 0.8828 | nan | 0.8828 | 0.0 | 0.8828 |
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+ | 0.0716 | 60.0 | 540 | 0.0756 | 0.4190 | 0.8380 | 0.8380 | nan | 0.8380 | 0.0 | 0.8380 |
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+ | 0.0464 | 62.22 | 560 | 0.1278 | 0.4503 | 0.9006 | 0.9006 | nan | 0.9006 | 0.0 | 0.9006 |
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+ | 0.0298 | 64.44 | 580 | 0.0867 | 0.4369 | 0.8737 | 0.8737 | nan | 0.8737 | 0.0 | 0.8737 |
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+ | 0.0493 | 66.67 | 600 | 0.0809 | 0.4378 | 0.8756 | 0.8756 | nan | 0.8756 | 0.0 | 0.8756 |
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+ | 0.0493 | 68.89 | 620 | 0.0620 | 0.4057 | 0.8113 | 0.8113 | nan | 0.8113 | 0.0 | 0.8113 |
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+ | 0.0426 | 71.11 | 640 | 0.0774 | 0.4361 | 0.8721 | 0.8721 | nan | 0.8721 | 0.0 | 0.8721 |
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+ | 0.0489 | 73.33 | 660 | 0.0705 | 0.4481 | 0.8961 | 0.8961 | nan | 0.8961 | 0.0 | 0.8961 |
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+ | 0.0442 | 75.56 | 680 | 0.0596 | 0.4316 | 0.8632 | 0.8632 | nan | 0.8632 | 0.0 | 0.8632 |
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+ | 0.0304 | 77.78 | 700 | 0.0985 | 0.4573 | 0.9146 | 0.9146 | nan | 0.9146 | 0.0 | 0.9146 |
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+ | 0.0386 | 80.0 | 720 | 0.0554 | 0.4391 | 0.8781 | 0.8781 | nan | 0.8781 | 0.0 | 0.8781 |
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+ | 0.0299 | 82.22 | 740 | 0.0936 | 0.4608 | 0.9215 | 0.9215 | nan | 0.9215 | 0.0 | 0.9215 |
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+ | 0.0469 | 84.44 | 760 | 0.1044 | 0.4751 | 0.9503 | 0.9503 | nan | 0.9503 | 0.0 | 0.9503 |
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+ | 0.0294 | 86.67 | 780 | 0.0469 | 0.4297 | 0.8593 | 0.8593 | nan | 0.8593 | 0.0 | 0.8593 |
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+ | 0.0363 | 88.89 | 800 | 0.0883 | 0.4624 | 0.9249 | 0.9249 | nan | 0.9249 | 0.0 | 0.9249 |
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+ | 0.0256 | 91.11 | 820 | 0.0388 | 0.4120 | 0.8241 | 0.8241 | nan | 0.8241 | 0.0 | 0.8241 |
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+ | 0.0302 | 93.33 | 840 | 0.0664 | 0.4562 | 0.9123 | 0.9123 | nan | 0.9123 | 0.0 | 0.9123 |
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+ | 0.0344 | 95.56 | 860 | 0.0905 | 0.4702 | 0.9403 | 0.9403 | nan | 0.9403 | 0.0 | 0.9403 |
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+ | 0.0322 | 97.78 | 880 | 0.0599 | 0.4528 | 0.9055 | 0.9055 | nan | 0.9055 | 0.0 | 0.9055 |
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+ | 0.0258 | 100.0 | 900 | 0.0718 | 0.4516 | 0.9032 | 0.9032 | nan | 0.9032 | 0.0 | 0.9032 |
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+ | 0.0335 | 102.22 | 920 | 0.0477 | 0.4350 | 0.8700 | 0.8700 | nan | 0.8700 | 0.0 | 0.8700 |
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+ | 0.0344 | 104.44 | 940 | 0.0584 | 0.4491 | 0.8983 | 0.8983 | nan | 0.8983 | 0.0 | 0.8983 |
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+ | 0.0333 | 106.67 | 960 | 0.0707 | 0.4572 | 0.9144 | 0.9144 | nan | 0.9144 | 0.0 | 0.9144 |
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+ | 0.0243 | 108.89 | 980 | 0.0708 | 0.4662 | 0.9325 | 0.9325 | nan | 0.9325 | 0.0 | 0.9325 |
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+ | 0.027 | 111.11 | 1000 | 0.0607 | 0.4515 | 0.9031 | 0.9031 | nan | 0.9031 | 0.0 | 0.9031 |
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+ | 0.0257 | 113.33 | 1020 | 0.0406 | 0.4296 | 0.8592 | 0.8592 | nan | 0.8592 | 0.0 | 0.8592 |
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+ | 0.0205 | 115.56 | 1040 | 0.0494 | 0.4514 | 0.9028 | 0.9028 | nan | 0.9028 | 0.0 | 0.9028 |
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+ | 0.0455 | 117.78 | 1060 | 0.0686 | 0.4630 | 0.9261 | 0.9261 | nan | 0.9261 | 0.0 | 0.9261 |
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+ | 0.0307 | 120.0 | 1080 | 0.0505 | 0.4542 | 0.9083 | 0.9083 | nan | 0.9083 | 0.0 | 0.9083 |
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+ | 0.0306 | 122.22 | 1100 | 0.0699 | 0.4692 | 0.9384 | 0.9384 | nan | 0.9384 | 0.0 | 0.9384 |
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+ | 0.023 | 124.44 | 1120 | 0.0495 | 0.4556 | 0.9112 | 0.9112 | nan | 0.9112 | 0.0 | 0.9112 |
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+ | 0.0221 | 126.67 | 1140 | 0.0387 | 0.4378 | 0.8757 | 0.8757 | nan | 0.8757 | 0.0 | 0.8757 |
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+ | 0.0291 | 128.89 | 1160 | 0.0329 | 0.4234 | 0.8468 | 0.8468 | nan | 0.8468 | 0.0 | 0.8468 |
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+ | 0.0321 | 131.11 | 1180 | 0.0557 | 0.4712 | 0.9424 | 0.9424 | nan | 0.9424 | 0.0 | 0.9424 |
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+ | 0.0344 | 133.33 | 1200 | 0.0559 | 0.4661 | 0.9322 | 0.9322 | nan | 0.9322 | 0.0 | 0.9322 |
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+ | 0.0284 | 135.56 | 1220 | 0.0405 | 0.4398 | 0.8796 | 0.8796 | nan | 0.8796 | 0.0 | 0.8796 |
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+ | 0.0301 | 137.78 | 1240 | 0.0503 | 0.4646 | 0.9292 | 0.9292 | nan | 0.9292 | 0.0 | 0.9292 |
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+ | 0.0317 | 140.0 | 1260 | 0.0330 | 0.4334 | 0.8667 | 0.8667 | nan | 0.8667 | 0.0 | 0.8667 |
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+ | 0.0424 | 142.22 | 1280 | 0.0398 | 0.4503 | 0.9007 | 0.9007 | nan | 0.9007 | 0.0 | 0.9007 |
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+ | 0.0232 | 144.44 | 1300 | 0.0423 | 0.4573 | 0.9146 | 0.9146 | nan | 0.9146 | 0.0 | 0.9146 |
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+ | 0.0297 | 146.67 | 1320 | 0.0442 | 0.4627 | 0.9254 | 0.9254 | nan | 0.9254 | 0.0 | 0.9254 |
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+ | 0.0298 | 148.89 | 1340 | 0.0396 | 0.4501 | 0.9002 | 0.9002 | nan | 0.9002 | 0.0 | 0.9002 |
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+ | 0.0225 | 151.11 | 1360 | 0.0334 | 0.4384 | 0.8767 | 0.8767 | nan | 0.8767 | 0.0 | 0.8767 |
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+ | 0.0343 | 153.33 | 1380 | 0.0394 | 0.4542 | 0.9085 | 0.9085 | nan | 0.9085 | 0.0 | 0.9085 |
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+ | 0.0181 | 155.56 | 1400 | 0.0413 | 0.4642 | 0.9284 | 0.9284 | nan | 0.9284 | 0.0 | 0.9284 |
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+ | 0.0306 | 157.78 | 1420 | 0.0316 | 0.4428 | 0.8857 | 0.8857 | nan | 0.8857 | 0.0 | 0.8857 |
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+ | 0.0204 | 160.0 | 1440 | 0.0417 | 0.4659 | 0.9318 | 0.9318 | nan | 0.9318 | 0.0 | 0.9318 |
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+ | 0.0277 | 162.22 | 1460 | 0.0332 | 0.4523 | 0.9046 | 0.9046 | nan | 0.9046 | 0.0 | 0.9046 |
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+ | 0.0184 | 164.44 | 1480 | 0.0383 | 0.4656 | 0.9311 | 0.9311 | nan | 0.9311 | 0.0 | 0.9311 |
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+ | 0.0254 | 166.67 | 1500 | 0.0436 | 0.4687 | 0.9374 | 0.9374 | nan | 0.9374 | 0.0 | 0.9374 |
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+ | 0.0293 | 168.89 | 1520 | 0.0285 | 0.4439 | 0.8877 | 0.8877 | nan | 0.8877 | 0.0 | 0.8877 |
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+ | 0.0176 | 171.11 | 1540 | 0.0305 | 0.4537 | 0.9074 | 0.9074 | nan | 0.9074 | 0.0 | 0.9074 |
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+ | 0.0282 | 173.33 | 1560 | 0.0341 | 0.4566 | 0.9133 | 0.9133 | nan | 0.9133 | 0.0 | 0.9133 |
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+ | 0.019 | 175.56 | 1580 | 0.0334 | 0.4578 | 0.9155 | 0.9155 | nan | 0.9155 | 0.0 | 0.9155 |
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+ | 0.0266 | 177.78 | 1600 | 0.0341 | 0.4603 | 0.9205 | 0.9205 | nan | 0.9205 | 0.0 | 0.9205 |
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+ | 0.0231 | 180.0 | 1620 | 0.0275 | 0.4419 | 0.8837 | 0.8837 | nan | 0.8837 | 0.0 | 0.8837 |
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+ | 0.0161 | 182.22 | 1640 | 0.0318 | 0.4606 | 0.9212 | 0.9212 | nan | 0.9212 | 0.0 | 0.9212 |
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+ | 0.0258 | 184.44 | 1660 | 0.0312 | 0.4512 | 0.9025 | 0.9025 | nan | 0.9025 | 0.0 | 0.9025 |
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+ | 0.0205 | 186.67 | 1680 | 0.0349 | 0.4657 | 0.9314 | 0.9314 | nan | 0.9314 | 0.0 | 0.9314 |
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+ | 0.0166 | 188.89 | 1700 | 0.0321 | 0.4628 | 0.9256 | 0.9256 | nan | 0.9256 | 0.0 | 0.9256 |
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+ | 0.0179 | 191.11 | 1720 | 0.0275 | 0.4603 | 0.9207 | 0.9207 | nan | 0.9207 | 0.0 | 0.9207 |
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+ | 0.0241 | 193.33 | 1740 | 0.0304 | 0.4611 | 0.9221 | 0.9221 | nan | 0.9221 | 0.0 | 0.9221 |
145
+ | 0.0219 | 195.56 | 1760 | 0.0317 | 0.4631 | 0.9261 | 0.9261 | nan | 0.9261 | 0.0 | 0.9261 |
146
+ | 0.0335 | 197.78 | 1780 | 0.0360 | 0.4677 | 0.9354 | 0.9354 | nan | 0.9354 | 0.0 | 0.9354 |
147
+ | 0.0149 | 200.0 | 1800 | 0.0317 | 0.4659 | 0.9318 | 0.9318 | nan | 0.9318 | 0.0 | 0.9318 |
148
+ | 0.0215 | 202.22 | 1820 | 0.0240 | 0.4419 | 0.8838 | 0.8838 | nan | 0.8838 | 0.0 | 0.8838 |
149
+ | 0.0274 | 204.44 | 1840 | 0.0211 | 0.4290 | 0.8581 | 0.8581 | nan | 0.8581 | 0.0 | 0.8581 |
150
+ | 0.015 | 206.67 | 1860 | 0.0224 | 0.4443 | 0.8885 | 0.8885 | nan | 0.8885 | 0.0 | 0.8885 |
151
+ | 0.0274 | 208.89 | 1880 | 0.0214 | 0.4420 | 0.8840 | 0.8840 | nan | 0.8840 | 0.0 | 0.8840 |
152
+ | 0.0169 | 211.11 | 1900 | 0.0206 | 0.4430 | 0.8859 | 0.8859 | nan | 0.8859 | 0.0 | 0.8859 |
153
+ | 0.0233 | 213.33 | 1920 | 0.0249 | 0.4581 | 0.9162 | 0.9162 | nan | 0.9162 | 0.0 | 0.9162 |
154
+ | 0.0155 | 215.56 | 1940 | 0.0301 | 0.4659 | 0.9318 | 0.9318 | nan | 0.9318 | 0.0 | 0.9318 |
155
+ | 0.0171 | 217.78 | 1960 | 0.0282 | 0.4601 | 0.9202 | 0.9202 | nan | 0.9202 | 0.0 | 0.9202 |
156
+ | 0.0128 | 220.0 | 1980 | 0.0281 | 0.4642 | 0.9283 | 0.9283 | nan | 0.9283 | 0.0 | 0.9283 |
157
+ | 0.0221 | 222.22 | 2000 | 0.0273 | 0.4578 | 0.9156 | 0.9156 | nan | 0.9156 | 0.0 | 0.9156 |
158
+ | 0.0194 | 224.44 | 2020 | 0.0244 | 0.4578 | 0.9156 | 0.9156 | nan | 0.9156 | 0.0 | 0.9156 |
159
+ | 0.0229 | 226.67 | 2040 | 0.0275 | 0.4683 | 0.9366 | 0.9366 | nan | 0.9366 | 0.0 | 0.9366 |
160
+ | 0.0148 | 228.89 | 2060 | 0.0308 | 0.4700 | 0.9400 | 0.9400 | nan | 0.9400 | 0.0 | 0.9400 |
161
+ | 0.0141 | 231.11 | 2080 | 0.0226 | 0.4567 | 0.9135 | 0.9135 | nan | 0.9135 | 0.0 | 0.9135 |
162
+ | 0.0143 | 233.33 | 2100 | 0.0260 | 0.4671 | 0.9342 | 0.9342 | nan | 0.9342 | 0.0 | 0.9342 |
163
+ | 0.0215 | 235.56 | 2120 | 0.0232 | 0.4544 | 0.9088 | 0.9088 | nan | 0.9088 | 0.0 | 0.9088 |
164
+ | 0.0171 | 237.78 | 2140 | 0.0232 | 0.4584 | 0.9168 | 0.9168 | nan | 0.9168 | 0.0 | 0.9168 |
165
+ | 0.0152 | 240.0 | 2160 | 0.0227 | 0.4533 | 0.9066 | 0.9066 | nan | 0.9066 | 0.0 | 0.9066 |
166
+ | 0.0232 | 242.22 | 2180 | 0.0228 | 0.4570 | 0.9139 | 0.9139 | nan | 0.9139 | 0.0 | 0.9139 |
167
+ | 0.0219 | 244.44 | 2200 | 0.0237 | 0.4575 | 0.9151 | 0.9151 | nan | 0.9151 | 0.0 | 0.9151 |
168
+ | 0.0206 | 246.67 | 2220 | 0.0269 | 0.4724 | 0.9447 | 0.9447 | nan | 0.9447 | 0.0 | 0.9447 |
169
+ | 0.013 | 248.89 | 2240 | 0.0237 | 0.4629 | 0.9257 | 0.9257 | nan | 0.9257 | 0.0 | 0.9257 |
170
+ | 0.0305 | 251.11 | 2260 | 0.0232 | 0.4621 | 0.9242 | 0.9242 | nan | 0.9242 | 0.0 | 0.9242 |
171
+ | 0.0235 | 253.33 | 2280 | 0.0208 | 0.4559 | 0.9118 | 0.9118 | nan | 0.9118 | 0.0 | 0.9118 |
172
+ | 0.014 | 255.56 | 2300 | 0.0233 | 0.4683 | 0.9366 | 0.9366 | nan | 0.9366 | 0.0 | 0.9366 |
173
+ | 0.022 | 257.78 | 2320 | 0.0214 | 0.4509 | 0.9018 | 0.9018 | nan | 0.9018 | 0.0 | 0.9018 |
174
+ | 0.013 | 260.0 | 2340 | 0.0210 | 0.4563 | 0.9126 | 0.9126 | nan | 0.9126 | 0.0 | 0.9126 |
175
+ | 0.0196 | 262.22 | 2360 | 0.0214 | 0.4637 | 0.9275 | 0.9275 | nan | 0.9275 | 0.0 | 0.9275 |
176
+ | 0.0148 | 264.44 | 2380 | 0.0216 | 0.4639 | 0.9278 | 0.9278 | nan | 0.9278 | 0.0 | 0.9278 |
177
+ | 0.0192 | 266.67 | 2400 | 0.0216 | 0.4654 | 0.9309 | 0.9309 | nan | 0.9309 | 0.0 | 0.9309 |
178
+ | 0.0183 | 268.89 | 2420 | 0.0197 | 0.4553 | 0.9106 | 0.9106 | nan | 0.9106 | 0.0 | 0.9106 |
179
+ | 0.0158 | 271.11 | 2440 | 0.0190 | 0.4565 | 0.9130 | 0.9130 | nan | 0.9130 | 0.0 | 0.9130 |
180
+ | 0.0191 | 273.33 | 2460 | 0.0202 | 0.4619 | 0.9238 | 0.9238 | nan | 0.9238 | 0.0 | 0.9238 |
181
+ | 0.0131 | 275.56 | 2480 | 0.0216 | 0.4623 | 0.9245 | 0.9245 | nan | 0.9245 | 0.0 | 0.9245 |
182
+ | 0.0138 | 277.78 | 2500 | 0.0199 | 0.4600 | 0.9201 | 0.9201 | nan | 0.9201 | 0.0 | 0.9201 |
183
+ | 0.015 | 280.0 | 2520 | 0.0183 | 0.4579 | 0.9157 | 0.9157 | nan | 0.9157 | 0.0 | 0.9157 |
184
+ | 0.0182 | 282.22 | 2540 | 0.0177 | 0.4555 | 0.9109 | 0.9109 | nan | 0.9109 | 0.0 | 0.9109 |
185
+ | 0.015 | 284.44 | 2560 | 0.0230 | 0.4727 | 0.9454 | 0.9454 | nan | 0.9454 | 0.0 | 0.9454 |
186
+ | 0.0188 | 286.67 | 2580 | 0.0200 | 0.4587 | 0.9174 | 0.9174 | nan | 0.9174 | 0.0 | 0.9174 |
187
+ | 0.012 | 288.89 | 2600 | 0.0203 | 0.4650 | 0.9301 | 0.9301 | nan | 0.9301 | 0.0 | 0.9301 |
188
+ | 0.0219 | 291.11 | 2620 | 0.0215 | 0.4687 | 0.9374 | 0.9374 | nan | 0.9374 | 0.0 | 0.9374 |
189
+ | 0.0222 | 293.33 | 2640 | 0.0186 | 0.4612 | 0.9224 | 0.9224 | nan | 0.9224 | 0.0 | 0.9224 |
190
+ | 0.0184 | 295.56 | 2660 | 0.0189 | 0.4575 | 0.9150 | 0.9150 | nan | 0.9150 | 0.0 | 0.9150 |
191
+ | 0.0202 | 297.78 | 2680 | 0.0192 | 0.4623 | 0.9245 | 0.9245 | nan | 0.9245 | 0.0 | 0.9245 |
192
+ | 0.0108 | 300.0 | 2700 | 0.0192 | 0.4615 | 0.9230 | 0.9230 | nan | 0.9230 | 0.0 | 0.9230 |
193
+ | 0.0154 | 302.22 | 2720 | 0.0187 | 0.4608 | 0.9216 | 0.9216 | nan | 0.9216 | 0.0 | 0.9216 |
194
+ | 0.0184 | 304.44 | 2740 | 0.0199 | 0.4659 | 0.9318 | 0.9318 | nan | 0.9318 | 0.0 | 0.9318 |
195
+ | 0.0141 | 306.67 | 2760 | 0.0211 | 0.4722 | 0.9445 | 0.9445 | nan | 0.9445 | 0.0 | 0.9445 |
196
+ | 0.0182 | 308.89 | 2780 | 0.0191 | 0.4640 | 0.9280 | 0.9280 | nan | 0.9280 | 0.0 | 0.9280 |
197
+ | 0.0137 | 311.11 | 2800 | 0.0162 | 0.4359 | 0.8717 | 0.8717 | nan | 0.8717 | 0.0 | 0.8717 |
198
+ | 0.0216 | 313.33 | 2820 | 0.0166 | 0.4597 | 0.9193 | 0.9193 | nan | 0.9193 | 0.0 | 0.9193 |
199
+ | 0.0212 | 315.56 | 2840 | 0.0168 | 0.4567 | 0.9134 | 0.9134 | nan | 0.9134 | 0.0 | 0.9134 |
200
+ | 0.0125 | 317.78 | 2860 | 0.0171 | 0.4606 | 0.9212 | 0.9212 | nan | 0.9212 | 0.0 | 0.9212 |
201
+ | 0.0105 | 320.0 | 2880 | 0.0175 | 0.4600 | 0.9200 | 0.9200 | nan | 0.9200 | 0.0 | 0.9200 |
202
+ | 0.0095 | 322.22 | 2900 | 0.0187 | 0.4671 | 0.9341 | 0.9341 | nan | 0.9341 | 0.0 | 0.9341 |
203
+ | 0.0245 | 324.44 | 2920 | 0.0170 | 0.4582 | 0.9165 | 0.9165 | nan | 0.9165 | 0.0 | 0.9165 |
204
+ | 0.0131 | 326.67 | 2940 | 0.0161 | 0.4570 | 0.9139 | 0.9139 | nan | 0.9139 | 0.0 | 0.9139 |
205
+ | 0.0178 | 328.89 | 2960 | 0.0179 | 0.4635 | 0.9270 | 0.9270 | nan | 0.9270 | 0.0 | 0.9270 |
206
+ | 0.0135 | 331.11 | 2980 | 0.0169 | 0.4604 | 0.9208 | 0.9208 | nan | 0.9208 | 0.0 | 0.9208 |
207
+ | 0.0177 | 333.33 | 3000 | 0.0167 | 0.4577 | 0.9153 | 0.9153 | nan | 0.9153 | 0.0 | 0.9153 |
208
+ | 0.0124 | 335.56 | 3020 | 0.0180 | 0.4674 | 0.9349 | 0.9349 | nan | 0.9349 | 0.0 | 0.9349 |
209
+ | 0.0143 | 337.78 | 3040 | 0.0157 | 0.4450 | 0.8900 | 0.8900 | nan | 0.8900 | 0.0 | 0.8900 |
210
+ | 0.0212 | 340.0 | 3060 | 0.0173 | 0.4643 | 0.9286 | 0.9286 | nan | 0.9286 | 0.0 | 0.9286 |
211
+ | 0.017 | 342.22 | 3080 | 0.0157 | 0.4518 | 0.9035 | 0.9035 | nan | 0.9035 | 0.0 | 0.9035 |
212
+ | 0.0193 | 344.44 | 3100 | 0.0167 | 0.4637 | 0.9274 | 0.9274 | nan | 0.9274 | 0.0 | 0.9274 |
213
+ | 0.0106 | 346.67 | 3120 | 0.0162 | 0.4531 | 0.9062 | 0.9062 | nan | 0.9062 | 0.0 | 0.9062 |
214
+ | 0.0195 | 348.89 | 3140 | 0.0177 | 0.4642 | 0.9284 | 0.9284 | nan | 0.9284 | 0.0 | 0.9284 |
215
+ | 0.0126 | 351.11 | 3160 | 0.0169 | 0.4702 | 0.9405 | 0.9405 | nan | 0.9405 | 0.0 | 0.9405 |
216
+ | 0.0117 | 353.33 | 3180 | 0.0151 | 0.4534 | 0.9068 | 0.9068 | nan | 0.9068 | 0.0 | 0.9068 |
217
+ | 0.0137 | 355.56 | 3200 | 0.0171 | 0.4685 | 0.9369 | 0.9369 | nan | 0.9369 | 0.0 | 0.9369 |
218
+ | 0.0169 | 357.78 | 3220 | 0.0153 | 0.4539 | 0.9078 | 0.9078 | nan | 0.9078 | 0.0 | 0.9078 |
219
+ | 0.0285 | 360.0 | 3240 | 0.0170 | 0.4609 | 0.9218 | 0.9218 | nan | 0.9218 | 0.0 | 0.9218 |
220
+ | 0.0194 | 362.22 | 3260 | 0.0166 | 0.4628 | 0.9256 | 0.9256 | nan | 0.9256 | 0.0 | 0.9256 |
221
+ | 0.0159 | 364.44 | 3280 | 0.0164 | 0.4601 | 0.9201 | 0.9201 | nan | 0.9201 | 0.0 | 0.9201 |
222
+ | 0.0095 | 366.67 | 3300 | 0.0146 | 0.4538 | 0.9076 | 0.9076 | nan | 0.9076 | 0.0 | 0.9076 |
223
+ | 0.017 | 368.89 | 3320 | 0.0153 | 0.4573 | 0.9145 | 0.9145 | nan | 0.9145 | 0.0 | 0.9145 |
224
+ | 0.0123 | 371.11 | 3340 | 0.0165 | 0.4672 | 0.9344 | 0.9344 | nan | 0.9344 | 0.0 | 0.9344 |
225
+ | 0.0213 | 373.33 | 3360 | 0.0165 | 0.4677 | 0.9353 | 0.9353 | nan | 0.9353 | 0.0 | 0.9353 |
226
+ | 0.0152 | 375.56 | 3380 | 0.0155 | 0.4645 | 0.9291 | 0.9291 | nan | 0.9291 | 0.0 | 0.9291 |
227
+ | 0.016 | 377.78 | 3400 | 0.0154 | 0.4514 | 0.9029 | 0.9029 | nan | 0.9029 | 0.0 | 0.9029 |
228
+ | 0.025 | 380.0 | 3420 | 0.0153 | 0.4553 | 0.9107 | 0.9107 | nan | 0.9107 | 0.0 | 0.9107 |
229
+ | 0.0107 | 382.22 | 3440 | 0.0168 | 0.4649 | 0.9299 | 0.9299 | nan | 0.9299 | 0.0 | 0.9299 |
230
+ | 0.0153 | 384.44 | 3460 | 0.0151 | 0.4607 | 0.9214 | 0.9214 | nan | 0.9214 | 0.0 | 0.9214 |
231
+ | 0.0095 | 386.67 | 3480 | 0.0142 | 0.4530 | 0.9061 | 0.9061 | nan | 0.9061 | 0.0 | 0.9061 |
232
+ | 0.0106 | 388.89 | 3500 | 0.0156 | 0.4634 | 0.9268 | 0.9268 | nan | 0.9268 | 0.0 | 0.9268 |
233
+ | 0.0111 | 391.11 | 3520 | 0.0157 | 0.4634 | 0.9268 | 0.9268 | nan | 0.9268 | 0.0 | 0.9268 |
234
+ | 0.0167 | 393.33 | 3540 | 0.0149 | 0.4613 | 0.9227 | 0.9227 | nan | 0.9227 | 0.0 | 0.9227 |
235
+ | 0.015 | 395.56 | 3560 | 0.0155 | 0.4673 | 0.9345 | 0.9345 | nan | 0.9345 | 0.0 | 0.9345 |
236
+ | 0.0109 | 397.78 | 3580 | 0.0159 | 0.4713 | 0.9426 | 0.9426 | nan | 0.9426 | 0.0 | 0.9426 |
237
+ | 0.013 | 400.0 | 3600 | 0.0162 | 0.4644 | 0.9287 | 0.9287 | nan | 0.9287 | 0.0 | 0.9287 |
238
+ | 0.017 | 402.22 | 3620 | 0.0146 | 0.4568 | 0.9137 | 0.9137 | nan | 0.9137 | 0.0 | 0.9137 |
239
+ | 0.0182 | 404.44 | 3640 | 0.0150 | 0.4629 | 0.9259 | 0.9259 | nan | 0.9259 | 0.0 | 0.9259 |
240
+ | 0.0122 | 406.67 | 3660 | 0.0155 | 0.4659 | 0.9318 | 0.9318 | nan | 0.9318 | 0.0 | 0.9318 |
241
+ | 0.0103 | 408.89 | 3680 | 0.0146 | 0.4604 | 0.9207 | 0.9207 | nan | 0.9207 | 0.0 | 0.9207 |
242
+ | 0.0144 | 411.11 | 3700 | 0.0153 | 0.4630 | 0.9260 | 0.9260 | nan | 0.9260 | 0.0 | 0.9260 |
243
+ | 0.013 | 413.33 | 3720 | 0.0141 | 0.4544 | 0.9089 | 0.9089 | nan | 0.9089 | 0.0 | 0.9089 |
244
+ | 0.019 | 415.56 | 3740 | 0.0162 | 0.4683 | 0.9366 | 0.9366 | nan | 0.9366 | 0.0 | 0.9366 |
245
+ | 0.0161 | 417.78 | 3760 | 0.0163 | 0.4705 | 0.9409 | 0.9409 | nan | 0.9409 | 0.0 | 0.9409 |
246
+ | 0.0198 | 420.0 | 3780 | 0.0158 | 0.4682 | 0.9365 | 0.9365 | nan | 0.9365 | 0.0 | 0.9365 |
247
+ | 0.01 | 422.22 | 3800 | 0.0145 | 0.4662 | 0.9323 | 0.9323 | nan | 0.9323 | 0.0 | 0.9323 |
248
+ | 0.0169 | 424.44 | 3820 | 0.0155 | 0.4674 | 0.9349 | 0.9349 | nan | 0.9349 | 0.0 | 0.9349 |
249
+ | 0.011 | 426.67 | 3840 | 0.0145 | 0.4622 | 0.9245 | 0.9245 | nan | 0.9245 | 0.0 | 0.9245 |
250
+ | 0.0117 | 428.89 | 3860 | 0.0139 | 0.4570 | 0.9139 | 0.9139 | nan | 0.9139 | 0.0 | 0.9139 |
251
+ | 0.0114 | 431.11 | 3880 | 0.0150 | 0.4638 | 0.9275 | 0.9275 | nan | 0.9275 | 0.0 | 0.9275 |
252
+ | 0.0185 | 433.33 | 3900 | 0.0150 | 0.4654 | 0.9309 | 0.9309 | nan | 0.9309 | 0.0 | 0.9309 |
253
+ | 0.0144 | 435.56 | 3920 | 0.0143 | 0.4647 | 0.9293 | 0.9293 | nan | 0.9293 | 0.0 | 0.9293 |
254
+ | 0.025 | 437.78 | 3940 | 0.0141 | 0.4623 | 0.9246 | 0.9246 | nan | 0.9246 | 0.0 | 0.9246 |
255
+ | 0.0142 | 440.0 | 3960 | 0.0150 | 0.4662 | 0.9323 | 0.9323 | nan | 0.9323 | 0.0 | 0.9323 |
256
+ | 0.0208 | 442.22 | 3980 | 0.0149 | 0.4648 | 0.9297 | 0.9297 | nan | 0.9297 | 0.0 | 0.9297 |
257
+ | 0.0113 | 444.44 | 4000 | 0.0149 | 0.4665 | 0.9330 | 0.9330 | nan | 0.9330 | 0.0 | 0.9330 |
258
+ | 0.01 | 446.67 | 4020 | 0.0151 | 0.4680 | 0.9359 | 0.9359 | nan | 0.9359 | 0.0 | 0.9359 |
259
+ | 0.012 | 448.89 | 4040 | 0.0151 | 0.4665 | 0.9331 | 0.9331 | nan | 0.9331 | 0.0 | 0.9331 |
260
+ | 0.0127 | 451.11 | 4060 | 0.0145 | 0.4624 | 0.9249 | 0.9249 | nan | 0.9249 | 0.0 | 0.9249 |
261
+ | 0.0183 | 453.33 | 4080 | 0.0135 | 0.4578 | 0.9156 | 0.9156 | nan | 0.9156 | 0.0 | 0.9156 |
262
+ | 0.0097 | 455.56 | 4100 | 0.0148 | 0.4659 | 0.9317 | 0.9317 | nan | 0.9317 | 0.0 | 0.9317 |
263
+ | 0.0107 | 457.78 | 4120 | 0.0139 | 0.4609 | 0.9218 | 0.9218 | nan | 0.9218 | 0.0 | 0.9218 |
264
+ | 0.0187 | 460.0 | 4140 | 0.0145 | 0.4645 | 0.9289 | 0.9289 | nan | 0.9289 | 0.0 | 0.9289 |
265
+ | 0.0231 | 462.22 | 4160 | 0.0135 | 0.4596 | 0.9192 | 0.9192 | nan | 0.9192 | 0.0 | 0.9192 |
266
+ | 0.0198 | 464.44 | 4180 | 0.0143 | 0.4605 | 0.9209 | 0.9209 | nan | 0.9209 | 0.0 | 0.9209 |
267
+ | 0.0172 | 466.67 | 4200 | 0.0141 | 0.4617 | 0.9234 | 0.9234 | nan | 0.9234 | 0.0 | 0.9234 |
268
+ | 0.0148 | 468.89 | 4220 | 0.0141 | 0.4594 | 0.9189 | 0.9189 | nan | 0.9189 | 0.0 | 0.9189 |
269
+ | 0.0167 | 471.11 | 4240 | 0.0137 | 0.4610 | 0.9220 | 0.9220 | nan | 0.9220 | 0.0 | 0.9220 |
270
+ | 0.0165 | 473.33 | 4260 | 0.0138 | 0.4602 | 0.9204 | 0.9204 | nan | 0.9204 | 0.0 | 0.9204 |
271
+ | 0.0137 | 475.56 | 4280 | 0.0136 | 0.4589 | 0.9177 | 0.9177 | nan | 0.9177 | 0.0 | 0.9177 |
272
+ | 0.0084 | 477.78 | 4300 | 0.0154 | 0.4704 | 0.9409 | 0.9409 | nan | 0.9409 | 0.0 | 0.9409 |
273
+ | 0.0087 | 480.0 | 4320 | 0.0150 | 0.4658 | 0.9315 | 0.9315 | nan | 0.9315 | 0.0 | 0.9315 |
274
+ | 0.0101 | 482.22 | 4340 | 0.0144 | 0.4651 | 0.9302 | 0.9302 | nan | 0.9302 | 0.0 | 0.9302 |
275
+ | 0.0168 | 484.44 | 4360 | 0.0144 | 0.4647 | 0.9295 | 0.9295 | nan | 0.9295 | 0.0 | 0.9295 |
276
+ | 0.0119 | 486.67 | 4380 | 0.0135 | 0.4620 | 0.9240 | 0.9240 | nan | 0.9240 | 0.0 | 0.9240 |
277
+ | 0.0093 | 488.89 | 4400 | 0.0133 | 0.4572 | 0.9144 | 0.9144 | nan | 0.9144 | 0.0 | 0.9144 |
278
+ | 0.015 | 491.11 | 4420 | 0.0132 | 0.4580 | 0.9160 | 0.9160 | nan | 0.9160 | 0.0 | 0.9160 |
279
+ | 0.0108 | 493.33 | 4440 | 0.0138 | 0.4585 | 0.9171 | 0.9171 | nan | 0.9171 | 0.0 | 0.9171 |
280
+ | 0.0147 | 495.56 | 4460 | 0.0140 | 0.4647 | 0.9294 | 0.9294 | nan | 0.9294 | 0.0 | 0.9294 |
281
+ | 0.0182 | 497.78 | 4480 | 0.0137 | 0.4599 | 0.9199 | 0.9199 | nan | 0.9199 | 0.0 | 0.9199 |
282
+ | 0.0191 | 500.0 | 4500 | 0.0136 | 0.4619 | 0.9238 | 0.9238 | nan | 0.9238 | 0.0 | 0.9238 |
283
+
284
+
285
+ ### Framework versions
286
+
287
+ - Transformers 4.37.2
288
+ - Pytorch 2.1.0+cu121
289
+ - Datasets 2.17.1
290
+ - Tokenizers 0.15.2
config.json ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "nvidia/mit-b0",
3
+ "architectures": [
4
+ "SegformerForSemanticSegmentation"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.0,
7
+ "classifier_dropout_prob": 0.1,
8
+ "decoder_hidden_size": 256,
9
+ "depths": [
10
+ 2,
11
+ 2,
12
+ 2,
13
+ 2
14
+ ],
15
+ "downsampling_rates": [
16
+ 1,
17
+ 4,
18
+ 8,
19
+ 16
20
+ ],
21
+ "drop_path_rate": 0.1,
22
+ "hidden_act": "gelu",
23
+ "hidden_dropout_prob": 0.0,
24
+ "hidden_sizes": [
25
+ 32,
26
+ 64,
27
+ 160,
28
+ 256
29
+ ],
30
+ "id2label": {
31
+ "0": "unlabeled",
32
+ "1": "lipid"
33
+ },
34
+ "image_size": 224,
35
+ "initializer_range": 0.02,
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