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samitizerxu
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kelp-from-scratch-segformer-b1-lr-0.0001-cleaned

Image Segmentation
Transformers
Safetensors
segformer
vision
Generated from Trainer
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  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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- ## Model Details
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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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- 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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- ### 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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  ---
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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: kelp-from-scratch-segformer-b1-lr-0.0001-cleaned
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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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+ # kelp-from-scratch-segformer-b1-lr-0.0001-cleaned
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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: 0.1757
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+ - Loss: 0.7377
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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: 0.0001
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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: 50
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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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+ | 0.9943 | 0.15 | 30 | 0.0093 | 0.9841 |
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+ | 0.9987 | 0.3 | 60 | 0.0091 | 0.9797 |
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+ | 0.9912 | 0.46 | 90 | 0.0093 | 0.9822 |
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+ | 0.9809 | 0.61 | 120 | 0.0089 | 0.9788 |
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+ | 0.9882 | 0.76 | 150 | 0.0096 | 0.9791 |
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+ | 0.9804 | 0.91 | 180 | 0.0070 | 0.9799 |
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+ | 0.9987 | 1.07 | 210 | 0.0104 | 0.9275 |
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+ | 0.997 | 1.22 | 240 | 0.0086 | 0.9696 |
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+ | 0.8985 | 1.37 | 270 | 0.0086 | 0.9513 |
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+ | 0.9921 | 1.52 | 300 | 0.0234 | 0.8703 |
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+ | 0.9656 | 1.68 | 330 | 0.0115 | 0.9610 |
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+ | 0.9229 | 1.83 | 360 | 0.0235 | 0.8871 |
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+ | 0.9999 | 1.98 | 390 | 0.0117 | 0.9416 |
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+ | 0.8409 | 2.13 | 420 | 0.0172 | 0.9212 |
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+ | 0.9533 | 2.28 | 450 | 0.0322 | 0.8209 |
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+ | 0.9815 | 2.44 | 480 | 0.0167 | 0.9070 |
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+ | 0.9567 | 2.59 | 510 | 0.0314 | 0.8028 |
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+ | 0.9747 | 2.74 | 540 | 0.0300 | 0.8489 |
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+ | 0.983 | 2.89 | 570 | 0.0176 | 0.9069 |
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+ | 0.963 | 3.05 | 600 | 0.0312 | 0.8878 |
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+ | 0.9616 | 3.2 | 630 | 0.0249 | 0.8482 |
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+ | 0.9395 | 3.35 | 660 | 0.0418 | 0.7781 |
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+ | 0.8966 | 3.5 | 690 | 0.0155 | 0.8981 |
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+ | 0.9939 | 3.65 | 720 | 0.0219 | 0.8737 |
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+ | 0.9895 | 3.81 | 750 | 0.0107 | 0.9480 |
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+ | 0.9434 | 3.96 | 780 | 0.0266 | 0.8413 |
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+ | 0.9813 | 4.11 | 810 | 0.0323 | 0.8320 |
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+ | 0.9051 | 4.26 | 840 | 0.0379 | 0.8097 |
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+ | 0.9629 | 4.42 | 870 | 0.0447 | 0.7619 |
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+ | 0.9067 | 4.57 | 900 | 0.0330 | 0.8137 |
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+ | 0.9245 | 4.72 | 930 | 0.0253 | 0.8327 |
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+ | 1.0 | 4.87 | 960 | 0.0273 | 0.8226 |
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+ | 0.9869 | 5.03 | 990 | 0.0182 | 0.8993 |
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+ | 0.9236 | 5.18 | 1020 | 0.0390 | 0.7772 |
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+ | 0.9487 | 5.33 | 1050 | 0.0433 | 0.7845 |
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+ | 0.9085 | 5.48 | 1080 | 0.0181 | 0.9285 |
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+ | 0.9518 | 5.63 | 1110 | 0.0514 | 0.7551 |
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+ | 0.9768 | 5.79 | 1140 | 0.0344 | 0.8475 |
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+ | 0.8779 | 5.94 | 1170 | 0.0123 | 0.9069 |
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+ | 0.9556 | 6.09 | 1200 | 0.0183 | 0.9066 |
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+ | 0.8875 | 6.24 | 1230 | 0.1934 | 0.7787 |
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+ | 0.9932 | 6.4 | 1260 | 0.0543 | 0.8902 |
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+ | 0.9991 | 6.55 | 1290 | 0.0467 | 0.7678 |
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+ | 0.9654 | 6.7 | 1320 | 0.0420 | 0.8434 |
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+ | 0.9794 | 6.85 | 1350 | 0.0190 | 0.9163 |
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+ | 0.9659 | 7.01 | 1380 | 0.0345 | 0.9441 |
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+ | 0.8959 | 7.16 | 1410 | 0.0255 | 0.8717 |
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+ | 0.9775 | 7.31 | 1440 | 0.0296 | 0.9072 |
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+ | 0.9406 | 7.46 | 1470 | 0.0331 | 0.8282 |
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+ | 0.9702 | 7.61 | 1500 | 0.0283 | 0.8532 |
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+ | 0.9828 | 7.77 | 1530 | 0.0164 | 0.8719 |
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+ | 0.9511 | 7.92 | 1560 | 0.0248 | 0.8392 |
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+ | 0.9046 | 8.07 | 1590 | 0.0116 | 0.9260 |
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+ | 0.9508 | 8.22 | 1620 | 0.0243 | 0.8499 |
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+ | 0.9535 | 8.38 | 1650 | 0.0185 | 0.8567 |
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+ | 0.9586 | 8.53 | 1680 | 0.0176 | 0.8867 |
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+ | 0.947 | 8.68 | 1710 | 0.0296 | 0.7973 |
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+ | 0.9404 | 8.83 | 1740 | 0.0137 | 0.8879 |
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+ | 1.0 | 8.98 | 1770 | 0.0227 | 0.8902 |
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+ | 0.9618 | 9.14 | 1800 | 0.0419 | 0.8119 |
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+ | 0.8463 | 9.29 | 1830 | 0.0500 | 0.8065 |
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+ | 0.9683 | 9.44 | 1860 | 0.0136 | 0.9266 |
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+ | 0.9087 | 9.59 | 1890 | 0.0357 | 0.8041 |
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+ | 0.946 | 9.75 | 1920 | 0.0426 | 0.8023 |
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+ | 0.9723 | 9.9 | 1950 | 0.0470 | 0.7823 |
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+ | 0.9487 | 10.05 | 1980 | 0.0218 | 0.8771 |
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+ | 0.9483 | 10.2 | 2010 | 0.0265 | 0.8317 |
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+ | 0.9678 | 10.36 | 2040 | 0.0512 | 0.7447 |
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+ | 0.9909 | 10.51 | 2070 | 0.0266 | 0.8505 |
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+ | 0.9688 | 10.66 | 2100 | 0.0315 | 0.8457 |
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+ | 0.9617 | 10.81 | 2130 | 0.0388 | 0.7916 |
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+ | 0.9105 | 10.96 | 2160 | 0.0641 | 0.7939 |
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+ | 0.9447 | 11.12 | 2190 | 0.0338 | 0.8046 |
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+ | 0.9127 | 11.27 | 2220 | 0.0883 | 0.7771 |
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+ | 0.9151 | 11.42 | 2250 | 0.0285 | 0.8568 |
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+ | 0.9339 | 11.57 | 2280 | 0.0773 | 0.7554 |
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+ | 1.0 | 11.73 | 2310 | 0.0452 | 0.7623 |
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+ | 0.9428 | 11.88 | 2340 | 0.0147 | 0.9230 |
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+ | 1.0 | 12.03 | 2370 | 0.0266 | 0.8265 |
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+ | 0.9432 | 12.18 | 2400 | 0.0284 | 0.8732 |
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+ | 0.9436 | 12.34 | 2430 | 0.0398 | 0.7938 |
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+ | 0.9772 | 12.49 | 2460 | 0.0345 | 0.8073 |
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+ | 0.9552 | 12.64 | 2490 | 0.0125 | 0.9084 |
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+ | 1.0 | 12.79 | 2520 | 0.0255 | 0.8099 |
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+ | 0.953 | 12.94 | 2550 | 0.0694 | 0.7384 |
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+ | 0.9225 | 13.1 | 2580 | 0.0286 | 0.8104 |
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+ | 0.9119 | 13.25 | 2610 | 0.0312 | 0.8538 |
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+ | 0.9726 | 13.4 | 2640 | 0.0511 | 0.7505 |
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+ | 0.9674 | 13.55 | 2670 | 0.0504 | 0.7473 |
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+ | 0.972 | 13.71 | 2700 | 0.0496 | 0.8009 |
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+ | 0.9238 | 13.86 | 2730 | 0.0179 | 0.8479 |
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+ | 0.9535 | 14.01 | 2760 | 0.1306 | 0.7997 |
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+ | 0.9509 | 14.16 | 2790 | 0.0254 | 0.8065 |
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+ | 0.8756 | 14.31 | 2820 | 0.0584 | 0.7479 |
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+ | 0.9335 | 14.47 | 2850 | 0.0297 | 0.7988 |
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+ | 0.956 | 14.62 | 2880 | 0.0311 | 0.8155 |
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+ | 0.9544 | 14.77 | 2910 | 0.0526 | 0.8159 |
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+ | 0.8577 | 14.92 | 2940 | 0.0770 | 0.7906 |
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+ | 0.965 | 15.08 | 2970 | 0.0709 | 0.7446 |
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+ | 0.987 | 15.23 | 3000 | 0.0479 | 0.7767 |
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+ | 0.9692 | 15.38 | 3030 | 0.0895 | 0.8900 |
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+ | 0.9602 | 15.53 | 3060 | 0.1262 | 0.8408 |
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+ | 0.9586 | 15.69 | 3090 | 0.0643 | 0.7370 |
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+ | 0.8871 | 15.84 | 3120 | 0.1753 | 0.7848 |
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+ | 0.9259 | 15.99 | 3150 | 0.1110 | 0.7571 |
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+ | 0.9463 | 16.14 | 3180 | 0.0651 | 0.7799 |
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+ | 0.9489 | 16.29 | 3210 | 0.0627 | 0.7776 |
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+ | 0.9814 | 16.45 | 3240 | 0.0476 | 0.7595 |
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+ | 0.926 | 16.6 | 3270 | 0.0588 | 0.7354 |
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+ | 0.8921 | 16.75 | 3300 | 0.0608 | 0.7637 |
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+ | 0.9722 | 16.9 | 3330 | 0.0404 | 0.7916 |
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+ | 0.9535 | 17.06 | 3360 | 0.0520 | 0.8781 |
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+ | 0.9442 | 17.21 | 3390 | 0.0326 | 0.8189 |
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+ | 0.945 | 17.36 | 3420 | 0.1141 | 0.8753 |
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+ | 0.9799 | 17.51 | 3450 | 0.0678 | 0.7472 |
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+ | 0.8504 | 17.66 | 3480 | 0.1633 | 0.8211 |
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+ | 1.0 | 17.82 | 3510 | 0.0479 | 0.7849 |
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+ | 0.9681 | 17.97 | 3540 | 0.0585 | 0.7741 |
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+ | 0.9492 | 18.12 | 3570 | 0.0656 | 0.7374 |
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+ | 0.9481 | 18.27 | 3600 | 0.0817 | 0.7382 |
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+ | 0.9405 | 18.43 | 3630 | 0.0805 | 0.9278 |
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+ | 0.8967 | 18.58 | 3660 | 0.0457 | 0.7692 |
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+ | 0.9215 | 18.73 | 3690 | 0.0615 | 0.8308 |
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+ | 0.9722 | 18.88 | 3720 | 0.1454 | 0.8367 |
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+ | 0.9352 | 19.04 | 3750 | 0.1014 | 0.7641 |
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+ | 0.9581 | 19.19 | 3780 | 0.1549 | 0.8425 |
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+ | 0.9438 | 19.34 | 3810 | 0.0689 | 0.7524 |
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+ | 0.976 | 19.49 | 3840 | 0.1321 | 0.8181 |
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+ | 0.9248 | 19.64 | 3870 | 0.1782 | 0.8164 |
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+ | 0.9114 | 19.8 | 3900 | 0.1553 | 0.7879 |
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+ | 0.8975 | 19.95 | 3930 | 0.1875 | 0.7522 |
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+ | 0.9696 | 20.1 | 3960 | 0.1521 | 0.8031 |
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+ | 0.9217 | 20.25 | 3990 | 0.0667 | 0.7436 |
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+ | 0.9375 | 20.41 | 4020 | 0.0902 | 0.9042 |
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+ | 0.886 | 20.56 | 4050 | 0.0672 | 0.7541 |
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+ | 0.9647 | 20.71 | 4080 | 0.1952 | 0.7983 |
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+ | 0.9029 | 20.86 | 4110 | 0.0600 | 0.8339 |
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+ | 0.9865 | 21.02 | 4140 | 0.0353 | 0.8191 |
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+ | 0.9348 | 21.17 | 4170 | 0.0683 | 0.9285 |
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+ | 0.965 | 21.32 | 4200 | 0.1153 | 0.7778 |
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+ | 0.9006 | 21.47 | 4230 | 0.2049 | 0.7928 |
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+ | 0.9726 | 21.62 | 4260 | 0.0687 | 0.7431 |
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+ | 0.8811 | 21.78 | 4290 | 0.1643 | 0.7903 |
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+ | 0.9622 | 21.93 | 4320 | 0.1069 | 0.7641 |
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+ | 0.9267 | 22.08 | 4350 | 0.0647 | 0.7764 |
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+ | 0.9729 | 22.23 | 4380 | 0.0770 | 0.7323 |
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+ | 0.951 | 22.39 | 4410 | 0.1069 | 0.8905 |
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+ | 0.976 | 22.54 | 4440 | 0.1024 | 0.7324 |
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+ | 0.9763 | 22.69 | 4470 | 0.0679 | 0.8286 |
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+ | 0.912 | 22.84 | 4500 | 0.1492 | 0.7784 |
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+ | 0.8856 | 22.99 | 4530 | 0.1400 | 0.7411 |
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+ | 0.9663 | 23.15 | 4560 | 0.1588 | 0.8195 |
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+ | 0.9577 | 23.3 | 4590 | 0.0532 | 0.7803 |
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+ | 0.9898 | 23.45 | 4620 | 0.1014 | 0.7892 |
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+ | 0.9079 | 23.6 | 4650 | 0.0457 | 0.7695 |
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+ | 0.9014 | 23.76 | 4680 | 0.1119 | 0.7742 |
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+ | 0.959 | 23.91 | 4710 | 0.0781 | 0.7461 |
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+ | 0.9762 | 24.06 | 4740 | 0.0852 | 0.8429 |
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+ | 0.952 | 24.21 | 4770 | 0.0978 | 0.7348 |
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+ | 0.9606 | 24.37 | 4800 | 0.0966 | 0.7263 |
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+ | 0.93 | 24.52 | 4830 | 0.0707 | 0.7334 |
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+ | 0.9514 | 24.67 | 4860 | 0.2207 | 0.7526 |
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+ | 0.9639 | 24.82 | 4890 | 0.0877 | 0.7545 |
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+ | 0.8319 | 24.97 | 4920 | 0.0751 | 0.7816 |
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+ | 0.959 | 25.13 | 4950 | 0.0457 | 0.7726 |
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+ | 0.9875 | 25.28 | 4980 | 0.0877 | 0.9052 |
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+ | 0.9567 | 25.43 | 5010 | 0.0875 | 0.7308 |
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+ | 0.8535 | 25.58 | 5040 | 0.1697 | 0.8189 |
218
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219
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220
+ | 0.9139 | 26.04 | 5130 | 0.0856 | 0.7604 |
221
+ | 0.9684 | 26.19 | 5160 | 0.1764 | 0.8378 |
222
+ | 0.9869 | 26.35 | 5190 | 0.0372 | 0.8459 |
223
+ | 0.9325 | 26.5 | 5220 | 0.2127 | 0.7494 |
224
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225
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226
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227
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228
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229
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230
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231
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232
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233
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234
+ | 0.9467 | 28.17 | 5550 | 0.0687 | 0.7867 |
235
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236
+ | 1.0 | 28.48 | 5610 | 0.0922 | 0.7728 |
237
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238
+ | 0.9198 | 28.78 | 5670 | 0.2101 | 0.7313 |
239
+ | 0.9667 | 28.93 | 5700 | 0.1985 | 0.7698 |
240
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241
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242
+ | 0.9313 | 29.39 | 5790 | 0.1197 | 0.7443 |
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244
+ | 0.9283 | 29.7 | 5850 | 0.1204 | 0.7310 |
245
+ | 0.8794 | 29.85 | 5880 | 0.2253 | 0.7703 |
246
+ | 0.9999 | 30.0 | 5910 | 0.0922 | 0.7463 |
247
+ | 1.0 | 30.15 | 5940 | 0.0763 | 0.7472 |
248
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249
+ | 0.9543 | 30.46 | 6000 | 0.1619 | 0.7388 |
250
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251
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254
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255
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256
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270
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+
379
+
380
+ ### Framework versions
381
+
382
+ - Transformers 4.37.1
383
+ - Pytorch 2.1.2
384
+ - Datasets 2.16.1
385
+ - Tokenizers 0.15.1
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