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samitizerxu
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segformer-b3-from-scratch-final

Image Segmentation
Transformers
Safetensors
segformer
vision
Generated from Trainer
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  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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-
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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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-
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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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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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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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-
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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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-
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- ### Training Data
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- [More Information Needed]
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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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-
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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-
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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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- [More Information Needed]
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-
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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-
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- ## Citation [optional]
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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-b3-from-scratch-final
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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-b3-from-scratch-final
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+
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+ This model is a fine-tuned version of [](https://huggingface.co/) on the samitizerxu/kelp_data_rgbagg_swin_nir_int_cleaned dataset.
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+ It achieves the following results on the evaluation set:
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+ - Iou Kelp: 0.0073
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+ - Loss: 0.9864
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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: 0.001
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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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: 60
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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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+ | 0.9993 | 0.18 | 100 | 0.0069 | 0.9867 |
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+ | 0.9945 | 0.37 | 200 | 0.0076 | 0.9855 |
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+ | 0.9991 | 0.55 | 300 | 0.0069 | 0.9867 |
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+ | 0.999 | 0.74 | 400 | 0.0066 | 0.9870 |
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+ | 0.9959 | 0.92 | 500 | 0.0071 | 0.9864 |
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+ | 0.9965 | 1.11 | 600 | 0.0066 | 0.9871 |
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+ | 0.9764 | 1.29 | 700 | 0.0066 | 0.9871 |
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+ | 0.9951 | 1.48 | 800 | 0.0066 | 0.9871 |
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+ | 0.9999 | 1.66 | 900 | 0.0066 | 0.9870 |
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+ | 0.9878 | 1.85 | 1000 | 0.0066 | 0.9871 |
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+ | 0.9978 | 2.03 | 1100 | 0.0066 | 0.9871 |
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+ | 0.975 | 2.21 | 1200 | 0.0069 | 0.9868 |
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+ | 0.9957 | 2.4 | 1300 | 0.0073 | 0.9859 |
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+ | 0.9914 | 2.58 | 1400 | 0.0079 | 0.9860 |
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+ | 0.9928 | 2.77 | 1500 | 0.0074 | 0.9859 |
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+ | 0.9994 | 2.95 | 1600 | 0.0004 | 0.9863 |
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+ | 0.995 | 3.14 | 1700 | 0.0101 | 0.9860 |
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+ | 0.9963 | 3.32 | 1800 | 0.0 | 0.9872 |
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+ | 0.9972 | 3.51 | 1900 | 0.0074 | 0.9858 |
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+ | 0.9959 | 3.69 | 2000 | 0.0076 | 0.9859 |
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+ | 0.9941 | 3.87 | 2100 | 0.0073 | 0.9859 |
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+ | 0.992 | 4.06 | 2200 | 0.0002 | 0.9951 |
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+ | 0.9903 | 4.24 | 2300 | 0.0073 | 0.9859 |
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+ | 0.9989 | 4.43 | 2400 | 0.0066 | 0.9871 |
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+ | 0.9999 | 4.61 | 2500 | 0.0073 | 0.9866 |
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+ | 0.9946 | 4.8 | 2600 | 0.0073 | 0.9859 |
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+ | 0.9959 | 4.98 | 2700 | 0.0073 | 0.9859 |
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+ | 0.9971 | 5.17 | 2800 | 0.0079 | 0.9863 |
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+ | 0.9949 | 5.35 | 2900 | 0.0074 | 0.9859 |
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+ | 0.9846 | 5.54 | 3000 | 0.0073 | 0.9859 |
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+ | 0.9941 | 5.72 | 3100 | 0.0074 | 0.9859 |
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+ | 0.9867 | 5.9 | 3200 | 0.0074 | 0.9858 |
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+ | 0.9857 | 6.09 | 3300 | 0.0074 | 0.9861 |
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+ | 0.9986 | 6.27 | 3400 | 0.0074 | 0.9859 |
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+ | 0.9927 | 6.46 | 3500 | 0.0074 | 0.9860 |
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+ | 0.998 | 6.64 | 3600 | 0.0075 | 0.9858 |
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+ | 0.9971 | 6.83 | 3700 | 0.0074 | 0.9859 |
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+ | 0.9951 | 7.01 | 3800 | 0.0074 | 0.9859 |
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+ | 0.9998 | 7.2 | 3900 | 0.0074 | 0.9861 |
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+ | 0.995 | 7.38 | 4000 | 0.0075 | 0.9858 |
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+ | 0.9912 | 7.56 | 4100 | 0.0072 | 0.9861 |
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+ | 0.9995 | 7.75 | 4200 | 0.0074 | 0.9858 |
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+ | 0.9934 | 7.93 | 4300 | 0.0074 | 0.9860 |
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+ | 0.9885 | 8.12 | 4400 | 0.0074 | 0.9860 |
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+ | 0.9937 | 8.3 | 4500 | 0.0075 | 0.9857 |
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+ | 0.9954 | 8.49 | 4600 | 0.0075 | 0.9857 |
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+ | 0.9794 | 8.67 | 4700 | 0.0074 | 0.9858 |
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+ | 0.9967 | 8.86 | 4800 | 0.0075 | 0.9857 |
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+ | 0.9954 | 9.04 | 4900 | 0.0074 | 0.9862 |
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+ | 0.9966 | 9.23 | 5000 | 0.0074 | 0.9859 |
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+ | 0.9953 | 9.41 | 5100 | 0.0074 | 0.9859 |
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+ | 0.9961 | 9.59 | 5200 | 0.0074 | 0.9859 |
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+ | 0.993 | 9.78 | 5300 | 0.0075 | 0.9858 |
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+ | 0.9993 | 9.96 | 5400 | 0.0070 | 0.9870 |
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+ | 0.995 | 10.15 | 5500 | 0.0032 | 0.9933 |
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+ | 0.9945 | 10.33 | 5600 | 0.0061 | 0.9884 |
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+ | 0.9738 | 10.52 | 5700 | 0.0069 | 0.9866 |
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+ | 0.9983 | 10.7 | 5800 | 0.0067 | 0.9869 |
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+ | 0.9975 | 10.89 | 5900 | 0.0076 | 0.9854 |
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+ | 0.9925 | 11.07 | 6000 | 0.0086 | 0.9839 |
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+ | 0.9821 | 11.25 | 6100 | 0.0092 | 0.9822 |
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+ | 0.9972 | 11.44 | 6200 | 0.0107 | 0.9787 |
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+ | 0.9802 | 11.62 | 6300 | 0.0109 | 0.9781 |
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+ | 1.0 | 11.81 | 6400 | 0.0076 | 0.9854 |
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+ | 0.9922 | 11.99 | 6500 | 0.0108 | 0.9793 |
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+ | 0.9915 | 12.18 | 6600 | 0.0108 | 0.9799 |
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+ | 0.9963 | 12.36 | 6700 | 0.0075 | 0.9857 |
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+ | 0.9966 | 12.55 | 6800 | 0.0075 | 0.9859 |
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+ | 0.9978 | 12.73 | 6900 | 0.0069 | 0.9870 |
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+ | 0.9847 | 12.92 | 7000 | 0.0074 | 0.9860 |
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+ | 0.9972 | 13.1 | 7100 | 0.0072 | 0.9862 |
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+ | 0.9868 | 13.28 | 7200 | 0.0071 | 0.9865 |
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+ | 0.9961 | 13.47 | 7300 | 0.0072 | 0.9864 |
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+ | 0.9845 | 13.65 | 7400 | 0.0071 | 0.9865 |
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+ | 0.9974 | 13.84 | 7500 | 0.0074 | 0.9862 |
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+ | 0.9906 | 14.02 | 7600 | 0.0076 | 0.9847 |
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+ | 0.9999 | 14.21 | 7700 | 0.0075 | 0.9860 |
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+ | 0.9821 | 14.39 | 7800 | 0.0074 | 0.9860 |
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+ | 0.9976 | 14.58 | 7900 | 0.0105 | 0.9795 |
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+ | 0.9871 | 14.76 | 8000 | 0.0103 | 0.9803 |
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+ | 0.991 | 14.94 | 8100 | 0.0102 | 0.9805 |
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+ | 0.9903 | 15.13 | 8200 | 0.0104 | 0.9799 |
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+ | 0.995 | 15.31 | 8300 | 0.0074 | 0.9861 |
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+ | 0.9981 | 15.5 | 8400 | 0.0073 | 0.9863 |
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+ | 0.9985 | 15.68 | 8500 | 0.0073 | 0.9863 |
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+ | 0.9973 | 15.87 | 8600 | 0.0074 | 0.9862 |
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+ | 0.989 | 16.05 | 8700 | 0.0073 | 0.9863 |
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+ | 0.9938 | 16.24 | 8800 | 0.0074 | 0.9860 |
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+ | 0.9951 | 16.42 | 8900 | 0.0106 | 0.9786 |
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+ | 0.9921 | 16.61 | 9000 | 0.0092 | 0.9824 |
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+ | 0.9971 | 16.79 | 9100 | 0.0083 | 0.9846 |
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+ | 0.9846 | 16.97 | 9200 | 0.0087 | 0.9838 |
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+ | 0.9849 | 17.16 | 9300 | 0.0095 | 0.9820 |
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+ | 0.9851 | 17.34 | 9400 | 0.0096 | 0.9818 |
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+ | 0.9902 | 17.53 | 9500 | 0.0099 | 0.9811 |
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+ | 0.9889 | 17.71 | 9600 | 0.0075 | 0.9860 |
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+ | 0.9782 | 17.9 | 9700 | 0.0075 | 0.9908 |
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+ | 0.999 | 18.08 | 9800 | 0.0074 | 0.9862 |
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+ | 0.9878 | 18.27 | 9900 | 0.0073 | 0.9862 |
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+ | 0.999 | 18.45 | 10000 | 0.0074 | 0.9862 |
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+ | 1.0 | 18.63 | 10100 | 0.0074 | 0.9861 |
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+ | 0.9951 | 18.82 | 10200 | 0.0075 | 0.9859 |
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+ | 0.9892 | 19.0 | 10300 | 0.0073 | 0.9861 |
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+ | 0.9853 | 19.19 | 10400 | 0.0074 | 0.9859 |
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+ | 0.9959 | 19.37 | 10500 | 0.0074 | 0.9859 |
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+ | 0.9999 | 19.56 | 10600 | 0.0073 | 0.9861 |
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+ | 0.9872 | 19.74 | 10700 | 0.0074 | 0.9859 |
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+ | 0.9939 | 19.93 | 10800 | 0.0074 | 0.9861 |
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+ | 0.9924 | 20.11 | 10900 | 0.0073 | 0.9862 |
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+ | 0.9993 | 20.3 | 11000 | 0.0074 | 0.9860 |
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+ | 0.9934 | 20.48 | 11100 | 0.0075 | 0.9858 |
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+ | 0.9976 | 20.66 | 11200 | 0.0074 | 0.9859 |
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+ | 0.9878 | 20.85 | 11300 | 0.0074 | 0.9859 |
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+ | 0.9955 | 21.03 | 11400 | 0.0074 | 0.9859 |
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+ | 0.9878 | 21.22 | 11500 | 0.0075 | 0.9859 |
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+ | 0.999 | 21.4 | 11600 | 0.0074 | 0.9859 |
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+ | 0.9945 | 21.59 | 11700 | 0.0074 | 0.9861 |
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+ | 0.994 | 21.77 | 11800 | 0.0075 | 0.9859 |
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+ | 0.9848 | 21.96 | 11900 | 0.0075 | 0.9859 |
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+ | 0.9998 | 22.14 | 12000 | 0.0075 | 0.9859 |
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+ | 0.9826 | 22.32 | 12100 | 0.0075 | 0.9859 |
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+ | 0.999 | 22.51 | 12200 | 0.0074 | 0.9861 |
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+ | 0.9941 | 22.69 | 12300 | 0.0073 | 0.9863 |
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+ | 0.9933 | 22.88 | 12400 | 0.0074 | 0.9862 |
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+ | 0.9935 | 23.06 | 12500 | 0.0074 | 0.9862 |
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+ | 0.9992 | 23.25 | 12600 | 0.0073 | 0.9863 |
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+ | 0.9943 | 23.43 | 12700 | 0.0073 | 0.9863 |
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+ | 0.9777 | 23.62 | 12800 | 0.0075 | 0.9858 |
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+ | 0.9977 | 23.8 | 12900 | 0.0073 | 0.9862 |
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+ | 0.9925 | 23.99 | 13000 | 0.0074 | 0.9861 |
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+ | 0.9866 | 24.17 | 13100 | 0.0073 | 0.9863 |
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+ | 0.9979 | 24.35 | 13200 | 0.0073 | 0.9862 |
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+ | 0.9819 | 24.54 | 13300 | 0.0073 | 0.9864 |
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+ | 0.966 | 24.72 | 13400 | 0.0073 | 0.9864 |
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+ | 0.998 | 24.91 | 13500 | 0.0073 | 0.9863 |
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+ | 0.9969 | 25.09 | 13600 | 0.0073 | 0.9863 |
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+ | 0.9881 | 25.28 | 13700 | 0.0073 | 0.9863 |
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+ | 0.9701 | 25.46 | 13800 | 0.0073 | 0.9864 |
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+ | 0.9963 | 25.65 | 13900 | 0.0073 | 0.9863 |
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+ | 0.9885 | 25.83 | 14000 | 0.0073 | 0.9863 |
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+ | 0.9904 | 26.01 | 14100 | 0.0073 | 0.9864 |
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+ | 0.9976 | 26.2 | 14200 | 0.0074 | 0.9862 |
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+ | 0.995 | 26.38 | 14300 | 0.0073 | 0.9863 |
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+ | 0.9886 | 26.57 | 14400 | 0.0073 | 0.9864 |
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+ | 0.9735 | 26.75 | 14500 | 0.0073 | 0.9863 |
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+ | 0.988 | 26.94 | 14600 | 0.0073 | 0.9864 |
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+ | 0.9854 | 27.12 | 14700 | 0.0073 | 0.9864 |
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+ | 0.9947 | 27.31 | 14800 | 0.0073 | 0.9864 |
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+ | 0.9944 | 27.49 | 14900 | 0.0073 | 0.9864 |
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+ | 0.9935 | 27.68 | 15000 | 0.0073 | 0.9862 |
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+ | 0.9887 | 27.86 | 15100 | 0.0073 | 0.9863 |
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+ | 0.9958 | 28.04 | 15200 | 0.0073 | 0.9862 |
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+ | 0.9994 | 28.23 | 15300 | 0.0073 | 0.9863 |
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+ | 0.9953 | 28.41 | 15400 | 0.0073 | 0.9868 |
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+ | 0.9798 | 28.6 | 15500 | 0.0073 | 0.9863 |
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+ | 0.9867 | 28.78 | 15600 | 0.0073 | 0.9863 |
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+ | 0.9903 | 28.97 | 15700 | 0.0073 | 0.9863 |
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+ | 0.9943 | 29.15 | 15800 | 0.0073 | 0.9864 |
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+ | 0.9725 | 29.34 | 15900 | 0.0072 | 0.9864 |
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+ | 0.9987 | 29.52 | 16000 | 0.0073 | 0.9864 |
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+ | 0.9871 | 29.7 | 16100 | 0.0072 | 0.9864 |
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+ | 0.992 | 29.89 | 16200 | 0.0072 | 0.9864 |
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+ | 0.996 | 30.07 | 16300 | 0.0073 | 0.9864 |
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+ | 0.9998 | 30.26 | 16400 | 0.0073 | 0.9864 |
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+ | 0.9964 | 30.44 | 16500 | 0.0074 | 0.9859 |
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+ | 0.9992 | 30.63 | 16600 | 0.0075 | 0.9858 |
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+ | 0.9946 | 30.81 | 16700 | 0.0074 | 0.9861 |
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+ | 0.9911 | 31.0 | 16800 | 0.0075 | 0.9859 |
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+ | 0.9878 | 31.18 | 16900 | 0.0075 | 0.9859 |
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+ | 0.9826 | 31.37 | 17000 | 0.0075 | 0.9859 |
220
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221
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222
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+
376
+
377
+ ### Framework versions
378
+
379
+ - Transformers 4.37.2
380
+ - Pytorch 2.2.0
381
+ - Datasets 2.17.0
382
+ - Tokenizers 0.15.1
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