| | --- |
| | license: other |
| | tags: |
| | - generated_from_trainer |
| | - Image_Masking |
| | model-index: |
| | - name: mit-b0-Image_segmentation-Carvana_Image_Masking |
| | results: [] |
| | language: |
| | - en |
| | metrics: |
| | - mean_iou |
| | pipeline_tag: image-segmentation |
| | --- |
| | |
| | # mit-b0-Image_segmentation-Carvana_Image_Masking |
| | |
| | This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0). |
| | |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0070 |
| | - Mean Iou: 0.9917 |
| | - Mean Accuracy: 0.9962 |
| | - Overall Accuracy: 0.9972 |
| | - Per Category Iou |
| | - Segment 0: 0.9964996655500316 |
| | - Segment 1: 0.9868763925617403 |
| | - Per Category Accuracy |
| | - Segment 0: 0.9980006976075766 |
| | - Segment 1: 0.994318466698934 |
| | |
| | ## Model description |
| | |
| | For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Segmentation/Carvana%20Image%20Masking/Carvana%20Image%20Masking%20-%20Image%20Segmentation%20with%20LoRA.ipynb |
| | |
| | ## Intended uses & limitations |
| | |
| | I used this to improve my skillset. I thank all of authors of the different technologies and dataset(s) for their contributions that have made this possible. |
| | |
| | Please make sure to properly cite the authors of the different technologies and dataset(s) as they absolutely deserve credit for their contributions. |
| | |
| | ## Training and evaluation data |
| | |
| | Dataset Source: https://www.kaggle.com/datasets/ipythonx/carvana-image-masking-png |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 0.0005 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Segment 0 Per Category Iou | Segment 1 Per Category Iou | Segment 0 Per Category Accuracy | Segment 1 Per Category Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------:|:--------------------:|:-----------------:|:--------------------:| |
| | | 0.0137 | 1.0 | 509 | 0.0113 | 0.9873 | 0.9942 | 0.9957 | 0.9946 | 0.9799 | 0.9969 | 0.9915 | |
| | | 0.011 | 2.0 | 1018 | 0.0096 | 0.9889 | 0.9948 | 0.9963 | 0.9953 | 0.9826 | 0.9974 | 0.9922 | |
| | | 0.0096 | 3.0 | 1527 | 0.0087 | 0.9899 | 0.9950 | 0.9966 | 0.9958 | 0.9841 | 0.9978 | 0.9922 | |
| | | 0.0089 | 4.0 | 2036 | 0.0082 | 0.9904 | 0.9958 | 0.9968 | 0.9959 | 0.9848 | 0.9975 | 0.9941 | |
| | | 0.0086 | 5.0 | 2545 | 0.0078 | 0.9907 | 0.9962 | 0.9969 | 0.9961 | 0.9853 | 0.9974 | 0.9951 | |
| | | 0.0082 | 6.0 | 3054 | 0.0077 | 0.9908 | 0.9964 | 0.9969 | 0.9961 | 0.9855 | 0.9973 | 0.9956 | |
| | | 0.0081 | 7.0 | 3563 | 0.0072 | 0.9914 | 0.9961 | 0.9971 | 0.9964 | 0.9864 | 0.9979 | 0.9944 | |
| | | 0.0081 | 8.0 | 4072 | 0.0071 | 0.9915 | 0.9961 | 0.9972 | 0.9964 | 0.9866 | 0.9980 | 0.9942 | |
| | | 0.0089 | 9.0 | 4581 | 0.0070 | 0.9916 | 0.9961 | 0.9972 | 0.9965 | 0.9868 | 0.9980 | 0.9941 | |
| | | 0.0076 | 10.0 | 5090 | 0.0070 | 0.9917 | 0.9962 | 0.9972 | 0.9965 | 0.9869 | 0.9980 | 0.9943 | |
| | |
| | * All values in the chart above are rounded to the nearest ten-thousandth. |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.29.1 |
| | - Pytorch 2.0.1 |
| | - Datasets 2.13.1 |
| | - Tokenizers 0.13.3 |
