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---
license: other
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 | 0.9200 | 0.9520 | 0.9778 | 0.974142444792198 | 0.8658711064023369 | 0.9896291184589182 | 0.9142864290038782 |
| 0.0185 | 5.0 | 430 | 0.1296 | 0.9182 | 0.9477 | 0.9775 | 0.9737715695013129 | 0.8627108292167807 | 0.9910418746696423 | 0.904378218719681 |
| 0.036 | 6.0 | 516 | 0.1410 | 0.9213 | 0.9538 | 0.9782 | 0.9745002408443008 | 0.8680673581922554 | 0.9892677512186527 | 0.9182967669045321 |
| 0.0376 | 7.0 | 602 | 0.1451 | 0.9206 | 0.9550 | 0.9779 | 0.9741455743906073 | 0.8669703237367214 | 0.9883004639689904 | 0.9216576612178001 |
| 0.0186 | 8.0 | 688 | 0.1380 | 0.9175 | 0.9496 | 0.9772 | 0.9733616852468584 | 0.8616466350192237 | 0.9897043519116697 | 0.9094762400541087 |
| 0.0162 | 9.0 | 774 | 0.1459 | 0.9218 | 0.9539 | 0.9783 | 0.9746840649852051 | 0.8688930149000804 | 0.989455276913138 | 0.9182917005479264 |
| 0.0169 | 10.0 | 860 | 0.1467 | 0.9191 | 0.9502 | 0.9776 | 0.9739086600912814 | 0.8642187978193332 | 0.9901195747929759 | 0.9102564589713776 |
| 0.0102 | 11.0 | 946 | 0.1549 | 0.9191 | 0.9524 | 0.9775 | 0.9737696499931041 | 0.8644247331609153 | 0.9889789745698009 | 0.915789237032027 |
| 0.0204 | 12.0 | 1032 | 0.1502 | 0.9215 | 0.9527 | 0.9783 | 0.974639596078376 | 0.8682964916021273 | 0.989902977623774 | 0.9155653673995151 |
| 0.0268 | 13.0 | 1118 | 0.1413 | 0.9194 | 0.9505 | 0.9777 | 0.9740020531855834 | 0.8647199376136 | 0.99011699066189 | 0.9107963425971664 |
| 0.0166 | 14.0 | 1204 | 0.1584 | 0.9173 | 0.9518 | 0.9770 | 0.9731154475737929 | 0.8614276032542578 | 0.9884142831972749 | 0.9152366875147241 |
| 0.0159 | 15.0 | 1290 | 0.1563 | 0.9170 | 0.9492 | 0.9770 | 0.9731832402253996 | 0.8607442858381036 | 0.9896456803899689 | 0.9087960816798012 |
| 0.0211 | 16.0 | 1376 | 0.1435 | 0.9150 | 0.9481 | 0.9764 | 0.9725201360275898 | 0.8574847000491036 | 0.989323310037 | 0.9068449010920532 |
| 0.0128 | 17.0 | 1462 | 0.1421 | 0.9212 | 0.9519 | 0.9782 | 0.9745789801464504 | 0.8677394402794754 | 0.9901920479238856 | 0.9136255861141298 |
| 0.0167 | 18.0 | 1548 | 0.1558 | 0.9217 | 0.9532 | 0.9783 | 0.9746811993626879 | 0.8686470009484697 | 0.9897428202266988 | 0.9166850322093621 |
| 0.0201 | 19.0 | 1634 | 0.1623 | 0.9156 | 0.9484 | 0.9766 | 0.9727184720007118 | 0.8584339325695252 | 0.9894484642039114 | 0.9072695251050635 |
| 0.0133 | 20.0 | 1720 | 0.1573 | 0.9189 | 0.9505 | 0.9776 | 0.9738320500157303 | 0.8640203613069115 | 0.9898665061373113 | 0.9112263496140702 |
| 0.012 | 21.0 | 1806 | 0.1631 | 0.9165 | 0.9472 | 0.9769 | 0.9731344243001482 | 0.8597866189796295 | 0.9904592118400188 | 0.9040137576913626 |
| 0.0148 | 22.0 | 1892 | 0.1629 | 0.9181 | 0.9507 | 0.9773 | 0.9735162429121835 | 0.8627239955489192 | 0.9894034768309156 | 0.9120129014770962 |
| 0.0137 | 23.0 | 1978 | 0.1701 | 0.9136 | 0.9484 | 0.9760 | 0.9719681843338751 | 0.8552607882028388 | 0.9885083690609032 | 0.908250815050119 |
| 0.0142 | 24.0 | 2064 | 0.1646 | 0.9146 | 0.9488 | 0.9763 | 0.9723134197764093 | 0.8568918401744342 | 0.9887405884771245 | 0.9089100747034281 |
| 0.0156 | 25.0 | 2150 | 0.1615 | 0.9144 | 0.9465 | 0.9763 | 0.9723929259786395 | 0.856345354289624 | 0.9898487696012216 | 0.9032139066422469 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3 |