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samitizerxu
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segformer-b1-from-scratch-run1

Image Segmentation
Transformers
Safetensors
segformer
vision
Generated from Trainer
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  1. README.md +327 -198
  2. config.json +69 -0
  3. model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md CHANGED
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
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  ---
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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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-
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- ## Model Details
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-
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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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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-
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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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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- ## Bias, Risks, and Limitations
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- ### Recommendations
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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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-
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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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- [More Information Needed]
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-
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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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- [More Information Needed]
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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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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- [More Information Needed]
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- ## Environmental Impact
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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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- - **Hardware Type:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ## Glossary [optional]
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- ## More Information [optional]
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  ---
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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-b1-from-scratch-run1
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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-b1-from-scratch-run1
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+
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+ This model is a fine-tuned version of [](https://huggingface.co/) on the samitizerxu/kelp_data_rgbaa_swin_nir dataset.
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+ It achieves the following results on the evaluation set:
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+ - Iou Kelp: 0.0067
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+ - Loss: 0.9872
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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: 0.1
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+ - train_batch_size: 22
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+ - eval_batch_size: 22
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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: cosine
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+ - num_epochs: 40
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Iou Kelp | Validation Loss |
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+ |:-------------:|:-----:|:----:|:--------:|:---------------:|
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+ | 0.9999 | 0.15 | 30 | 0.0067 | 0.9872 |
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+ | 1.0 | 0.29 | 60 | 0.0067 | 0.9872 |
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+ | 0.9933 | 0.44 | 90 | 0.0067 | 0.9872 |
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+ | 0.998 | 0.59 | 120 | 0.0067 | 0.9872 |
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+ | 1.0 | 0.73 | 150 | 0.0067 | 0.9872 |
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+ | 0.9998 | 0.88 | 180 | 0.0067 | 0.9872 |
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+ | 0.9998 | 1.02 | 210 | 0.0067 | 0.9872 |
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+ | 1.0 | 1.17 | 240 | 0.0082 | 0.9861 |
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+ | 0.9976 | 1.32 | 270 | 0.0069 | 0.9869 |
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+ | 0.9995 | 1.46 | 300 | 0.0070 | 0.9868 |
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+ | 0.9967 | 1.61 | 330 | 0.0067 | 0.9872 |
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+ | 0.9945 | 1.76 | 360 | 0.0067 | 0.9872 |
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+ | 1.0 | 1.9 | 390 | 0.0067 | 0.9872 |
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+ | 0.9992 | 2.05 | 420 | 0.0067 | 0.9872 |
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+ | 0.9991 | 2.2 | 450 | 0.0067 | 0.9872 |
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+ | 0.997 | 2.34 | 480 | 0.0067 | 0.9872 |
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+ | 0.999 | 2.49 | 510 | 0.0067 | 0.9872 |
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+ | 0.9999 | 2.63 | 540 | 0.0067 | 0.9872 |
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+ | 0.9991 | 2.78 | 570 | 0.0067 | 0.9872 |
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+ | 0.9987 | 2.93 | 600 | 0.0067 | 0.9872 |
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+ | 0.9999 | 3.07 | 630 | 0.0067 | 0.9872 |
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+ | 0.9983 | 3.22 | 660 | 0.0067 | 0.9872 |
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+ | 0.9973 | 3.37 | 690 | 0.0067 | 0.9872 |
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+ | 0.9987 | 3.51 | 720 | 0.0067 | 0.9872 |
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+ | 0.9915 | 3.66 | 750 | 0.0067 | 0.9872 |
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+ | 0.9984 | 3.8 | 780 | 0.0067 | 0.9872 |
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+ | 0.9992 | 3.95 | 810 | 0.0067 | 0.9872 |
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+ | 0.9993 | 4.1 | 840 | 0.0067 | 0.9872 |
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+ | 1.0 | 4.24 | 870 | 0.0067 | 0.9872 |
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+ | 0.9998 | 4.39 | 900 | 0.0067 | 0.9872 |
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+ | 0.9999 | 4.54 | 930 | 0.0067 | 0.9872 |
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+ | 0.9995 | 4.68 | 960 | 0.0067 | 0.9872 |
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+ | 0.998 | 4.83 | 990 | 0.0067 | 0.9872 |
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+ | 0.9989 | 4.98 | 1020 | 0.0067 | 0.9872 |
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+ | 0.9975 | 5.12 | 1050 | 0.0067 | 0.9872 |
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+ | 0.9993 | 5.27 | 1080 | 0.0067 | 0.9872 |
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+ | 0.9971 | 5.41 | 1110 | 0.0067 | 0.9872 |
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+ | 0.9944 | 5.56 | 1140 | 0.0067 | 0.9872 |
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+ | 0.9967 | 5.71 | 1170 | 0.0067 | 0.9872 |
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+ | 0.9986 | 5.85 | 1200 | 0.0067 | 0.9872 |
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+ | 0.9994 | 6.0 | 1230 | 0.0067 | 0.9872 |
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+ | 0.9997 | 6.15 | 1260 | 0.0067 | 0.9872 |
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+ | 0.9998 | 6.29 | 1290 | 0.0067 | 0.9872 |
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+ | 0.999 | 6.44 | 1320 | 0.0067 | 0.9872 |
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+ | 0.9996 | 6.59 | 1350 | 0.0067 | 0.9872 |
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+ | 1.0 | 6.73 | 1380 | 0.0067 | 0.9872 |
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+ | 0.9999 | 6.88 | 1410 | 0.0067 | 0.9872 |
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+ | 0.9933 | 7.02 | 1440 | 0.0067 | 0.9872 |
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+ | 0.998 | 7.17 | 1470 | 0.0067 | 0.9872 |
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+ | 0.9968 | 7.32 | 1500 | 0.0067 | 0.9872 |
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+ | 0.997 | 7.46 | 1530 | 0.0067 | 0.9872 |
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+ | 0.9981 | 7.61 | 1560 | 0.0067 | 0.9872 |
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+ | 0.9992 | 7.76 | 1590 | 0.0067 | 0.9872 |
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+ | 0.9999 | 7.9 | 1620 | 0.0067 | 0.9872 |
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+ | 0.9964 | 8.05 | 1650 | 0.0067 | 0.9872 |
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+ | 0.9999 | 8.2 | 1680 | 0.0067 | 0.9872 |
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+ | 0.9941 | 8.34 | 1710 | 0.0067 | 0.9872 |
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+ | 0.9963 | 8.49 | 1740 | 0.0067 | 0.9872 |
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+ | 0.998 | 8.63 | 1770 | 0.0067 | 0.9872 |
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+ | 0.9989 | 8.78 | 1800 | 0.0067 | 0.9872 |
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+ | 1.0 | 8.93 | 1830 | 0.0067 | 0.9872 |
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+ | 1.0 | 9.07 | 1860 | 0.0067 | 0.9872 |
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+ | 0.9974 | 9.22 | 1890 | 0.0067 | 0.9872 |
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+ | 0.9989 | 9.37 | 1920 | 0.0067 | 0.9872 |
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+ | 0.9989 | 9.51 | 1950 | 0.0067 | 0.9872 |
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+ | 0.996 | 9.66 | 1980 | 0.0067 | 0.9872 |
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+ | 0.9995 | 9.8 | 2010 | 0.0067 | 0.9872 |
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+ | 0.9973 | 9.95 | 2040 | 0.0067 | 0.9872 |
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+ | 0.9957 | 10.1 | 2070 | 0.0067 | 0.9872 |
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+ | 0.9996 | 10.24 | 2100 | 0.0067 | 0.9872 |
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+ | 1.0 | 10.39 | 2130 | 0.0067 | 0.9872 |
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+ | 0.9967 | 10.54 | 2160 | 0.0067 | 0.9872 |
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+ | 0.9989 | 10.68 | 2190 | 0.0067 | 0.9872 |
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+ | 0.9989 | 10.83 | 2220 | 0.0067 | 0.9872 |
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+ | 0.9994 | 10.98 | 2250 | 0.0067 | 0.9872 |
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+ | 0.9992 | 11.12 | 2280 | 0.0067 | 0.9872 |
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+ | 0.9973 | 11.27 | 2310 | 0.0067 | 0.9872 |
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+ | 0.9993 | 11.41 | 2340 | 0.0067 | 0.9872 |
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+ | 0.9973 | 11.56 | 2370 | 0.0067 | 0.9872 |
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+ | 0.9996 | 11.71 | 2400 | 0.0067 | 0.9872 |
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+ | 1.0 | 11.85 | 2430 | 0.0067 | 0.9872 |
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+ | 0.9989 | 12.0 | 2460 | 0.0067 | 0.9872 |
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+ | 1.0 | 12.15 | 2490 | 0.0067 | 0.9872 |
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+ | 0.9987 | 12.29 | 2520 | 0.0067 | 0.9872 |
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+ | 0.9914 | 12.44 | 2550 | 0.0067 | 0.9872 |
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+ | 0.9974 | 12.59 | 2580 | 0.0067 | 0.9872 |
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+ | 1.0 | 12.73 | 2610 | 0.0067 | 0.9872 |
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+ | 0.999 | 12.88 | 2640 | 0.0067 | 0.9872 |
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+ | 1.0 | 13.02 | 2670 | 0.0067 | 0.9872 |
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+ | 0.9991 | 13.17 | 2700 | 0.0067 | 0.9872 |
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+ | 0.9979 | 13.32 | 2730 | 0.0067 | 0.9872 |
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+ | 1.0 | 13.46 | 2760 | 0.0067 | 0.9872 |
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+ | 0.9973 | 13.61 | 2790 | 0.0067 | 0.9872 |
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+ | 0.9995 | 13.76 | 2820 | 0.0067 | 0.9872 |
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+ | 0.9973 | 13.9 | 2850 | 0.0067 | 0.9872 |
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+ | 0.9961 | 14.05 | 2880 | 0.0067 | 0.9872 |
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+ | 0.9907 | 14.2 | 2910 | 0.0067 | 0.9872 |
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+ | 0.9984 | 14.34 | 2940 | 0.0067 | 0.9872 |
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+ | 0.9986 | 14.49 | 2970 | 0.0067 | 0.9872 |
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+ | 0.9935 | 14.63 | 3000 | 0.0067 | 0.9872 |
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+ | 0.998 | 14.78 | 3030 | 0.0067 | 0.9872 |
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+ | 0.9982 | 14.93 | 3060 | 0.0067 | 0.9872 |
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+ | 0.9956 | 15.07 | 3090 | 0.0067 | 0.9872 |
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+ | 0.9991 | 15.22 | 3120 | 0.0067 | 0.9872 |
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+ | 0.9985 | 15.37 | 3150 | 0.0067 | 0.9872 |
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+ | 0.9958 | 15.51 | 3180 | 0.0067 | 0.9872 |
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+ | 0.9998 | 15.66 | 3210 | 0.0067 | 0.9872 |
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+ | 0.9972 | 15.8 | 3240 | 0.0067 | 0.9872 |
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+ | 0.9996 | 15.95 | 3270 | 0.0067 | 0.9872 |
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+ | 0.9965 | 16.1 | 3300 | 0.0067 | 0.9872 |
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+ | 0.9983 | 16.24 | 3330 | 0.0067 | 0.9872 |
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+ | 0.9993 | 16.39 | 3360 | 0.0067 | 0.9872 |
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+ | 0.9962 | 16.54 | 3390 | 0.0067 | 0.9872 |
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+ | 0.9985 | 16.68 | 3420 | 0.0067 | 0.9872 |
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+ | 0.9998 | 16.83 | 3450 | 0.0067 | 0.9872 |
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+ | 0.9993 | 16.98 | 3480 | 0.0067 | 0.9872 |
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+ | 0.9993 | 17.12 | 3510 | 0.0067 | 0.9872 |
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+ | 0.9998 | 17.27 | 3540 | 0.0067 | 0.9872 |
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+ | 1.0 | 17.41 | 3570 | 0.0067 | 0.9872 |
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+ | 0.9999 | 17.56 | 3600 | 0.0067 | 0.9872 |
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+ | 0.9993 | 17.71 | 3630 | 0.0067 | 0.9872 |
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+ | 0.999 | 17.85 | 3660 | 0.0067 | 0.9872 |
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+ | 0.9975 | 18.0 | 3690 | 0.0067 | 0.9872 |
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+ | 0.9993 | 18.15 | 3720 | 0.0067 | 0.9872 |
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+ | 1.0 | 18.29 | 3750 | 0.0067 | 0.9872 |
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+ | 0.9983 | 18.44 | 3780 | 0.0067 | 0.9872 |
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+ | 0.9994 | 18.59 | 3810 | 0.0067 | 0.9872 |
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+ | 0.9993 | 18.73 | 3840 | 0.0067 | 0.9872 |
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+ | 0.9982 | 18.88 | 3870 | 0.0067 | 0.9872 |
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+ | 0.9997 | 19.02 | 3900 | 0.0067 | 0.9872 |
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+ | 0.9955 | 19.17 | 3930 | 0.0067 | 0.9872 |
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+ | 0.9992 | 19.32 | 3960 | 0.0067 | 0.9872 |
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+ | 0.9592 | 19.46 | 3990 | 0.0067 | 0.9872 |
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+ | 0.9897 | 19.61 | 4020 | 0.0067 | 0.9872 |
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+ | 0.9994 | 19.76 | 4050 | 0.0067 | 0.9872 |
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+ | 0.9989 | 19.9 | 4080 | 0.0067 | 0.9872 |
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+ | 0.9995 | 20.05 | 4110 | 0.0067 | 0.9872 |
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+ | 0.9995 | 20.2 | 4140 | 0.0067 | 0.9872 |
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+ | 0.9938 | 20.34 | 4170 | 0.0067 | 0.9872 |
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+ | 0.9987 | 20.49 | 4200 | 0.0067 | 0.9872 |
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+ | 0.9999 | 20.63 | 4230 | 0.0067 | 0.9872 |
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+ | 0.9994 | 20.78 | 4260 | 0.0067 | 0.9872 |
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+ | 0.9954 | 20.93 | 4290 | 0.0067 | 0.9872 |
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+ | 0.9975 | 21.07 | 4320 | 0.0067 | 0.9872 |
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+ | 0.9997 | 21.22 | 4350 | 0.0067 | 0.9872 |
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+ | 0.9978 | 21.37 | 4380 | 0.0067 | 0.9872 |
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+ | 0.9994 | 21.51 | 4410 | 0.0067 | 0.9872 |
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+ | 0.9985 | 21.66 | 4440 | 0.0067 | 0.9872 |
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+ | 0.9998 | 21.8 | 4470 | 0.0067 | 0.9872 |
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+ | 0.998 | 21.95 | 4500 | 0.0067 | 0.9872 |
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+ | 0.9983 | 22.1 | 4530 | 0.0067 | 0.9872 |
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+ | 0.9989 | 22.24 | 4560 | 0.0067 | 0.9872 |
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+ | 0.9973 | 22.39 | 4590 | 0.0067 | 0.9872 |
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+ | 0.9961 | 22.54 | 4620 | 0.0067 | 0.9872 |
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+ | 0.9984 | 22.68 | 4650 | 0.0067 | 0.9872 |
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+ | 1.0 | 22.83 | 4680 | 0.0067 | 0.9872 |
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+ | 0.9949 | 22.98 | 4710 | 0.0067 | 0.9872 |
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+ | 0.9989 | 23.12 | 4740 | 0.0067 | 0.9872 |
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+ | 0.9998 | 23.27 | 4770 | 0.0067 | 0.9872 |
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+ | 0.9999 | 23.41 | 4800 | 0.0067 | 0.9872 |
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+ | 0.9996 | 23.56 | 4830 | 0.0067 | 0.9872 |
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+ | 0.9974 | 23.71 | 4860 | 0.0067 | 0.9872 |
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+ | 0.9997 | 23.85 | 4890 | 0.0067 | 0.9872 |
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+ | 0.9999 | 24.0 | 4920 | 0.0067 | 0.9872 |
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+ | 0.9962 | 24.15 | 4950 | 0.0067 | 0.9872 |
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+ | 0.9996 | 24.29 | 4980 | 0.0067 | 0.9872 |
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+ | 0.9999 | 24.44 | 5010 | 0.0067 | 0.9872 |
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+ | 0.9973 | 24.59 | 5040 | 0.0067 | 0.9872 |
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+ | 0.9996 | 24.73 | 5070 | 0.0067 | 0.9872 |
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+ | 0.9995 | 24.88 | 5100 | 0.0067 | 0.9872 |
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+ | 0.9999 | 25.02 | 5130 | 0.0067 | 0.9872 |
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+ | 0.9988 | 25.17 | 5160 | 0.0067 | 0.9872 |
222
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+
324
+
325
+ ### Framework versions
326
+
327
+ - Transformers 4.37.1
328
+ - Pytorch 2.1.2
329
+ - Datasets 2.16.1
330
+ - Tokenizers 0.15.1
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