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samitizerxu
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segformer-b1-improved

Image Segmentation
Transformers
Safetensors
segformer
vision
Generated from Trainer
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  2. config.json +76 -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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- 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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- ### Direct Use
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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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-
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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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- ### 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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- [More Information Needed]
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- ## Evaluation
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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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- ### 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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-
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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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- - **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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- ## Glossary [optional]
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- ## More Information [optional]
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  ---
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+ license: other
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+ base_model: nvidia/mit-b1
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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-improved
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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-improved
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+
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+ This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the samitizerxu/kelp_data dataset.
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+ It achieves the following results on the evaluation set:
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+ - Iou Kelp: 0.0
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+ - Loss: 0.0024
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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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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+ - lr_scheduler_warmup_ratio: 0.2
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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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+ | 0.0769 | 0.11 | 30 | 0.0 | 0.0650 |
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+ | 0.075 | 0.21 | 60 | 0.0 | 0.0710 |
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+ | 0.0701 | 0.32 | 90 | 0.0 | 0.0738 |
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+ | 0.0704 | 0.43 | 120 | 0.0 | 0.0710 |
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+ | 0.0669 | 0.53 | 150 | 0.0 | 0.0699 |
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+ | 0.0694 | 0.64 | 180 | 0.0 | 0.0679 |
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+ | 0.0635 | 0.74 | 210 | 0.0 | 0.0676 |
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+ | 0.0584 | 0.85 | 240 | 0.0 | 0.0647 |
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+ | 0.0552 | 0.96 | 270 | 0.0 | 0.0631 |
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+ | 0.0525 | 1.06 | 300 | 0.0 | 0.0592 |
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+ | 0.052 | 1.17 | 330 | 0.0 | 0.0540 |
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+ | 0.0449 | 1.28 | 360 | 0.0 | 0.0517 |
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+ | 0.0443 | 1.38 | 390 | 0.0 | 0.0459 |
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+ | 0.0364 | 1.49 | 420 | 0.0 | 0.0422 |
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+ | 0.0361 | 1.6 | 450 | 0.0 | 0.0374 |
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+ | 0.0339 | 1.7 | 480 | 0.0 | 0.0352 |
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+ | 0.0307 | 1.81 | 510 | 0.0 | 0.0319 |
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+ | 0.0283 | 1.91 | 540 | 0.0 | 0.0283 |
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+ | 0.0302 | 2.02 | 570 | 0.0 | 0.0275 |
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+ | 0.0277 | 2.13 | 600 | 0.0 | 0.0252 |
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+ | 0.0231 | 2.23 | 630 | 0.0 | 0.0223 |
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+ | 0.0261 | 2.34 | 660 | 0.0 | 0.0219 |
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+ | 0.025 | 2.45 | 690 | 0.0 | 0.0183 |
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+ | 0.0262 | 2.55 | 720 | 0.0 | 0.0163 |
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+ | 0.0182 | 2.66 | 750 | 0.0 | 0.0175 |
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+ | 0.0186 | 2.77 | 780 | 0.0 | 0.0147 |
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+ | 0.0156 | 2.87 | 810 | 0.0 | 0.0151 |
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+ | 0.0171 | 2.98 | 840 | 0.0 | 0.0148 |
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+ | 0.0164 | 3.09 | 870 | 0.0 | 0.0161 |
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+ | 0.0155 | 3.19 | 900 | 0.0 | 0.0139 |
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+ | 0.0127 | 3.3 | 930 | 0.0 | 0.0126 |
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+ | 0.0111 | 3.4 | 960 | 0.0 | 0.0105 |
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+ | 0.0139 | 3.51 | 990 | 0.0 | 0.0103 |
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+ | 0.0113 | 3.62 | 1020 | 0.0 | 0.0096 |
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+ | 0.0093 | 3.72 | 1050 | 0.0 | 0.0088 |
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+ | 0.0099 | 3.83 | 1080 | 0.0 | 0.0096 |
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+ | 0.0092 | 3.94 | 1110 | 0.0 | 0.0096 |
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+ | 0.0095 | 4.04 | 1140 | 0.0 | 0.0083 |
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+ | 0.0113 | 4.15 | 1170 | 0.0 | 0.0086 |
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+ | 0.0084 | 4.26 | 1200 | 0.0 | 0.0073 |
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+ | 0.0066 | 4.36 | 1230 | 0.0 | 0.0081 |
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+ | 0.01 | 4.47 | 1260 | 0.0 | 0.0074 |
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+ | 0.0062 | 4.57 | 1290 | 0.0 | 0.0067 |
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+ | 0.0053 | 4.68 | 1320 | 0.0 | 0.0065 |
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+ | 0.0073 | 4.79 | 1350 | 0.0 | 0.0058 |
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+ | 0.0069 | 4.89 | 1380 | 0.0 | 0.0065 |
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+ | 0.0063 | 5.0 | 1410 | 0.0 | 0.0060 |
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+ | 0.0062 | 5.11 | 1440 | 0.0 | 0.0055 |
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+ | 0.0069 | 5.21 | 1470 | 0.0 | 0.0058 |
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+ | 0.0049 | 5.32 | 1500 | 0.0 | 0.0055 |
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+ | 0.0047 | 5.43 | 1530 | 0.0 | 0.0050 |
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+ | 0.0092 | 5.53 | 1560 | 0.0 | 0.0054 |
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+ | 0.0041 | 5.64 | 1590 | 0.0 | 0.0049 |
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+ | 0.0042 | 5.74 | 1620 | 0.0 | 0.0049 |
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+ | 0.005 | 5.85 | 1650 | 0.0 | 0.0044 |
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+ | 0.0036 | 5.96 | 1680 | 0.0 | 0.0047 |
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+ | 0.0059 | 6.06 | 1710 | 0.0 | 0.0048 |
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+ | 0.0078 | 6.17 | 1740 | 0.0 | 0.0045 |
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+ | 0.0036 | 6.28 | 1770 | 0.0 | 0.0042 |
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+ | 0.004 | 6.38 | 1800 | 0.0 | 0.0039 |
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+ | 0.0038 | 6.49 | 1830 | 0.0 | 0.0041 |
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+ | 0.0038 | 6.6 | 1860 | 0.0 | 0.0042 |
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+ | 0.0028 | 6.7 | 1890 | 0.0 | 0.0042 |
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+ | 0.0036 | 6.81 | 1920 | 0.0 | 0.0036 |
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+ | 0.0032 | 6.91 | 1950 | 0.0 | 0.0037 |
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+ | 0.0029 | 7.02 | 1980 | 0.0 | 0.0035 |
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+ | 0.0022 | 7.13 | 2010 | 0.0 | 0.0036 |
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+ | 0.0025 | 7.23 | 2040 | 0.0 | 0.0036 |
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+ | 0.0024 | 7.34 | 2070 | 0.0 | 0.0035 |
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+ | 0.0022 | 7.45 | 2100 | 0.0 | 0.0032 |
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+ | 0.0031 | 7.55 | 2130 | 0.0 | 0.0033 |
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+ | 0.0028 | 7.66 | 2160 | 0.0 | 0.0031 |
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+ | 0.0022 | 7.77 | 2190 | 0.0 | 0.0032 |
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+ | 0.0023 | 7.87 | 2220 | 0.0 | 0.0034 |
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+ | 0.0021 | 7.98 | 2250 | 0.0 | 0.0033 |
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+ | 0.0018 | 8.09 | 2280 | 0.0 | 0.0031 |
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+ | 0.0021 | 8.19 | 2310 | 0.0 | 0.0032 |
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+ | 0.0038 | 8.3 | 2340 | 0.0 | 0.0030 |
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+ | 0.0016 | 8.4 | 2370 | 0.0 | 0.0031 |
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+ | 0.0066 | 8.51 | 2400 | 0.0 | 0.0029 |
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+ | 0.0015 | 8.62 | 2430 | 0.0 | 0.0030 |
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+ | 0.0022 | 8.72 | 2460 | 0.0 | 0.0028 |
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+ | 0.0022 | 8.83 | 2490 | 0.0 | 0.0028 |
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+ | 0.0012 | 8.94 | 2520 | 0.0 | 0.0027 |
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+ | 0.0017 | 9.04 | 2550 | 0.0 | 0.0028 |
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+ | 0.0019 | 9.15 | 2580 | 0.0 | 0.0027 |
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+ | 0.0014 | 9.26 | 2610 | 0.0 | 0.0028 |
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+ | 0.0014 | 9.36 | 2640 | 0.0 | 0.0028 |
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+ | 0.0013 | 9.47 | 2670 | 0.0 | 0.0027 |
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+ | 0.0014 | 9.57 | 2700 | 0.0 | 0.0027 |
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+ | 0.0015 | 9.68 | 2730 | 0.0 | 0.0028 |
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+ | 0.0014 | 9.79 | 2760 | 0.0 | 0.0027 |
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+ | 0.001 | 9.89 | 2790 | 0.0 | 0.0026 |
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+ | 0.0013 | 10.0 | 2820 | 0.0 | 0.0026 |
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+ | 0.0014 | 10.11 | 2850 | 0.0 | 0.0026 |
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+ | 0.0014 | 10.21 | 2880 | 0.0 | 0.0026 |
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+ | 0.0031 | 10.32 | 2910 | 0.0 | 0.0025 |
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+ | 0.0013 | 10.43 | 2940 | 0.0 | 0.0026 |
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+ | 0.0012 | 10.53 | 2970 | 0.0 | 0.0026 |
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+ | 0.0011 | 10.64 | 3000 | 0.0 | 0.0025 |
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+ | 0.0009 | 10.74 | 3030 | 0.0 | 0.0025 |
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+ | 0.0016 | 10.85 | 3060 | 0.0 | 0.0025 |
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+ | 0.0014 | 10.96 | 3090 | 0.0 | 0.0025 |
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+ | 0.0008 | 11.06 | 3120 | 0.0 | 0.0025 |
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+ | 0.0008 | 11.17 | 3150 | 0.0 | 0.0025 |
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+ | 0.0019 | 11.28 | 3180 | 0.0 | 0.0026 |
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+ | 0.001 | 11.38 | 3210 | 0.0 | 0.0025 |
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+ | 0.0009 | 11.49 | 3240 | 0.0 | 0.0025 |
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+ | 0.0014 | 11.6 | 3270 | 0.0 | 0.0026 |
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+ | 0.0014 | 11.7 | 3300 | 0.0 | 0.0024 |
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+ | 0.0008 | 11.81 | 3330 | 0.0 | 0.0025 |
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+ | 0.001 | 11.91 | 3360 | 0.0 | 0.0024 |
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+ | 0.0012 | 12.02 | 3390 | 0.0 | 0.0024 |
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+ | 0.0009 | 12.13 | 3420 | 0.0 | 0.0024 |
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+ | 0.0007 | 12.23 | 3450 | 0.0 | 0.0025 |
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+ | 0.0011 | 12.34 | 3480 | 0.0 | 0.0024 |
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+ | 0.0012 | 12.45 | 3510 | 0.0 | 0.0023 |
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+ | 0.0008 | 12.55 | 3540 | 0.0 | 0.0023 |
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+ | 0.0005 | 12.66 | 3570 | 0.0 | 0.0024 |
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+ | 0.0007 | 12.77 | 3600 | 0.0 | 0.0023 |
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+ | 0.0006 | 12.87 | 3630 | 0.0 | 0.0024 |
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+ | 0.0006 | 12.98 | 3660 | 0.0 | 0.0024 |
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+ | 0.0014 | 13.09 | 3690 | 0.0 | 0.0025 |
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+ | 0.0008 | 13.19 | 3720 | 0.0 | 0.0023 |
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+ | 0.0007 | 13.3 | 3750 | 0.0 | 0.0022 |
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+ | 0.0005 | 13.4 | 3780 | 0.0 | 0.0022 |
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+ | 0.0006 | 13.51 | 3810 | 0.0 | 0.0022 |
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+ | 0.0005 | 13.62 | 3840 | 0.0 | 0.0023 |
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+ | 0.0007 | 13.72 | 3870 | 0.0 | 0.0022 |
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+ | 0.0009 | 13.83 | 3900 | 0.0 | 0.0023 |
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+ | 0.0005 | 13.94 | 3930 | 0.0 | 0.0022 |
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+ | 0.0006 | 14.04 | 3960 | 0.0 | 0.0023 |
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+ | 0.0005 | 14.15 | 3990 | 0.0 | 0.0023 |
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+ | 0.0005 | 14.26 | 4020 | 0.0 | 0.0023 |
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+ | 0.0008 | 14.36 | 4050 | 0.0 | 0.0022 |
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+ | 0.0006 | 14.47 | 4080 | 0.0 | 0.0022 |
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+ | 0.001 | 14.57 | 4110 | 0.0 | 0.0022 |
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+ | 0.0004 | 14.68 | 4140 | 0.0 | 0.0021 |
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+ | 0.0005 | 14.79 | 4170 | 0.0 | 0.0022 |
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+ | 0.0006 | 14.89 | 4200 | 0.0 | 0.0023 |
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+ | 0.0006 | 15.0 | 4230 | 0.0 | 0.0023 |
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+ | 0.0007 | 15.11 | 4260 | 0.0 | 0.0022 |
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+ | 0.0006 | 15.21 | 4290 | 0.0 | 0.0022 |
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+ | 0.0005 | 15.32 | 4320 | 0.0 | 0.0021 |
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+ | 0.0004 | 15.43 | 4350 | 0.0 | 0.0021 |
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+ | 0.0008 | 15.53 | 4380 | 0.0 | 0.0021 |
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+ | 0.0005 | 15.64 | 4410 | 0.0 | 0.0021 |
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+ | 0.0005 | 15.74 | 4440 | 0.0 | 0.0022 |
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+ | 0.0003 | 15.85 | 4470 | 0.0 | 0.0021 |
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+ | 0.0005 | 15.96 | 4500 | 0.0 | 0.0021 |
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+ | 0.0004 | 16.06 | 4530 | 0.0 | 0.0023 |
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+ | 0.0004 | 16.17 | 4560 | 0.0 | 0.0022 |
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+ | 0.0004 | 16.28 | 4590 | 0.0 | 0.0021 |
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+ | 0.0005 | 16.38 | 4620 | 0.0 | 0.0020 |
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+ | 0.0007 | 16.49 | 4650 | 0.0 | 0.0022 |
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+ | 0.0005 | 16.6 | 4680 | 0.0 | 0.0021 |
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+ | 0.0011 | 16.7 | 4710 | 0.0 | 0.0020 |
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+ | 0.0005 | 16.81 | 4740 | 0.0 | 0.0020 |
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+ | 0.0005 | 16.91 | 4770 | 0.0 | 0.0022 |
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+ | 0.0004 | 17.02 | 4800 | 0.0 | 0.0021 |
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+ | 0.0004 | 17.13 | 4830 | 0.0 | 0.0022 |
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+ | 0.0004 | 17.23 | 4860 | 0.0 | 0.0021 |
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+ | 0.0007 | 17.34 | 4890 | 0.0 | 0.0021 |
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+ | 0.0006 | 17.45 | 4920 | 0.0 | 0.0021 |
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+ | 0.0006 | 17.55 | 4950 | 0.0 | 0.0021 |
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+ | 0.001 | 17.66 | 4980 | 0.0 | 0.0021 |
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+ | 0.0003 | 17.77 | 5010 | 0.0 | 0.0020 |
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+ | 0.0002 | 17.87 | 5040 | 0.0 | 0.0021 |
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+ | 0.0005 | 17.98 | 5070 | 0.0 | 0.0021 |
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+ | 0.0005 | 18.09 | 5100 | 0.0 | 0.0021 |
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+ | 0.0004 | 18.19 | 5130 | 0.0 | 0.0021 |
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+ | 0.0008 | 18.3 | 5160 | 0.0 | 0.0023 |
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+ | 0.0004 | 18.4 | 5190 | 0.0 | 0.0020 |
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+ | 0.0002 | 18.51 | 5220 | 0.0 | 0.0021 |
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+ | 0.0003 | 18.62 | 5250 | 0.0 | 0.0020 |
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+ | 0.0006 | 18.72 | 5280 | 0.0 | 0.0021 |
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+ | 0.0004 | 18.83 | 5310 | 0.0 | 0.0021 |
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+ | 0.0005 | 18.94 | 5340 | 0.0 | 0.0020 |
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+ | 0.0004 | 19.04 | 5370 | 0.0 | 0.0020 |
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+ | 0.0007 | 19.15 | 5400 | 0.0 | 0.0020 |
233
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+
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+
431
+ ### Framework versions
432
+
433
+ - Transformers 4.37.1
434
+ - Pytorch 2.1.2
435
+ - Datasets 2.16.1
436
+ - Tokenizers 0.15.1
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