---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mit-b0-Image_segmentation_Dominoes_v2
results: []
datasets:
- adelavega/dominoes_raw
language:
- en
metrics:
- mean_iou
pipeline_tag: image-segmentation
---
# mit-b0-Image_segmentation_Dominoes_v2
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: 0.1149
- Mean Iou: 0.9198
- Mean Accuracy: 0.9515
- Overall Accuracy: 0.9778
- Per Category Iou:
- Segment 0: 0.974110559111975
- Segment 1: 0.8655745252092782
- Per Category Accuracy
- Segment 0: 0.9897833441005461
- Segment 1: 0.913253525550903
## 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/Dominoes/Fine-Tuning%20-%20Dominoes%20-%20Image%20Segmentation%20with%20LoRA.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://huggingface.co/datasets/adelavega/dominoes_raw
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou Segment 0 | Per Category Iou Segment 1 | Per Category Accuracy Segment 0 | Per Category Accuracy Segment 1|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------------:|:---------------------:|:-----------------:|
| 0.0461 | 1.0 | 86 | 0.1233 | 0.9150 | 0.9527 | 0.9762 | 0.9721967854031923 | 0.8578619172251059 | 0.9869082633464498 | 0.9184139264010376 |
| 0.0708 | 2.0 | 172 | 0.1366 | 0.9172 | 0.9490 | 0.9771 | 0.9732821853093164 | 0.8611008788165083 | 0.9898473600751747 | 0.9082362492748777 |
| 0.048 | 3.0 | 258 | 0.1260 | 0.9199 | 0.9534 | 0.9777 | 0.9740118174014271 | 0.8658241844233872 | 0.9888392553004053 | 0.9179240730467295 |
| 0.0535 | 4.0 | 344 | 0.1184 | 0.92...
