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mit-b0-Image_segmentation_Dominoes_v2

Image Segmentation
PyTorch
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+ ---
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+ license: other
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: mit-b0-Image_segmentation_Dominoes_v2
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+ results: []
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+ ---
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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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+ # mit-b0-Image_segmentation_Dominoes_v2
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1149
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+ - Mean Iou: 0.9198
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+ - Mean Accuracy: 0.9515
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+ - Overall Accuracy: 0.9778
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+ - Per Category Iou: [0.974110559111975, 0.8655745252092782]
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+ - Per Category Accuracy: [0.9897833441005461, 0.913253525550903]
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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: 0.0005
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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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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+ - num_epochs: 25
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------:|:----------------------------------------:|
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+ | 0.0461 | 1.0 | 86 | 0.1233 | 0.9150 | 0.9527 | 0.9762 | [0.9721967854031923, 0.8578619172251059] | [0.9869082633464498, 0.9184139264010376] |
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+ | 0.0708 | 2.0 | 172 | 0.1366 | 0.9172 | 0.9490 | 0.9771 | [0.9732821853093164, 0.8611008788165083] | [0.9898473600751747, 0.9082362492748777] |
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+ | 0.048 | 3.0 | 258 | 0.1260 | 0.9199 | 0.9534 | 0.9777 | [0.9740118174014271, 0.8658241844233872] | [0.9888392553004053, 0.9179240730467295] |
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+ | 0.0535 | 4.0 | 344 | 0.1184 | 0.9200 | 0.9520 | 0.9778 | [0.974142444792198, 0.8658711064023369] | [0.9896291184589182, 0.9142864290038782] |
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+ | 0.0185 | 5.0 | 430 | 0.1296 | 0.9182 | 0.9477 | 0.9775 | [0.9737715695013129, 0.8627108292167807] | [0.9910418746696423, 0.904378218719681] |
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+ | 0.036 | 6.0 | 516 | 0.1410 | 0.9213 | 0.9538 | 0.9782 | [0.9745002408443008, 0.8680673581922554] | [0.9892677512186527, 0.9182967669045321] |
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+ | 0.0376 | 7.0 | 602 | 0.1451 | 0.9206 | 0.9550 | 0.9779 | [0.9741455743906073, 0.8669703237367214] | [0.9883004639689904, 0.9216576612178001] |
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+ | 0.0186 | 8.0 | 688 | 0.1380 | 0.9175 | 0.9496 | 0.9772 | [0.9733616852468584, 0.8616466350192237] | [0.9897043519116697, 0.9094762400541087] |
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+ | 0.0162 | 9.0 | 774 | 0.1459 | 0.9218 | 0.9539 | 0.9783 | [0.9746840649852051, 0.8688930149000804] | [0.989455276913138, 0.9182917005479264] |
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+ | 0.0169 | 10.0 | 860 | 0.1467 | 0.9191 | 0.9502 | 0.9776 | [0.9739086600912814, 0.8642187978193332] | [0.9901195747929759, 0.9102564589713776] |
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+ | 0.0102 | 11.0 | 946 | 0.1549 | 0.9191 | 0.9524 | 0.9775 | [0.9737696499931041, 0.8644247331609153] | [0.9889789745698009, 0.915789237032027] |
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+ | 0.0204 | 12.0 | 1032 | 0.1502 | 0.9215 | 0.9527 | 0.9783 | [0.974639596078376, 0.8682964916021273] | [0.989902977623774, 0.9155653673995151] |
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+ | 0.0268 | 13.0 | 1118 | 0.1413 | 0.9194 | 0.9505 | 0.9777 | [0.9740020531855834, 0.8647199376136] | [0.99011699066189, 0.9107963425971664] |
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+ | 0.0166 | 14.0 | 1204 | 0.1584 | 0.9173 | 0.9518 | 0.9770 | [0.9731154475737929, 0.8614276032542578] | [0.9884142831972749, 0.9152366875147241] |
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+ | 0.0159 | 15.0 | 1290 | 0.1563 | 0.9170 | 0.9492 | 0.9770 | [0.9731832402253996, 0.8607442858381036] | [0.9896456803899689, 0.9087960816798012] |
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+ | 0.0211 | 16.0 | 1376 | 0.1435 | 0.9150 | 0.9481 | 0.9764 | [0.9725201360275898, 0.8574847000491036] | [0.989323310037, 0.9068449010920532] |
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+ | 0.0128 | 17.0 | 1462 | 0.1421 | 0.9212 | 0.9519 | 0.9782 | [0.9745789801464504, 0.8677394402794754] | [0.9901920479238856, 0.9136255861141298] |
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+ | 0.0167 | 18.0 | 1548 | 0.1558 | 0.9217 | 0.9532 | 0.9783 | [0.9746811993626879, 0.8686470009484697] | [0.9897428202266988, 0.9166850322093621] |
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+ | 0.0201 | 19.0 | 1634 | 0.1623 | 0.9156 | 0.9484 | 0.9766 | [0.9727184720007118, 0.8584339325695252] | [0.9894484642039114, 0.9072695251050635] |
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+ | 0.0133 | 20.0 | 1720 | 0.1573 | 0.9189 | 0.9505 | 0.9776 | [0.9738320500157303, 0.8640203613069115] | [0.9898665061373113, 0.9112263496140702] |
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+ | 0.012 | 21.0 | 1806 | 0.1631 | 0.9165 | 0.9472 | 0.9769 | [0.9731344243001482, 0.8597866189796295] | [0.9904592118400188, 0.9040137576913626] |
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+ | 0.0148 | 22.0 | 1892 | 0.1629 | 0.9181 | 0.9507 | 0.9773 | [0.9735162429121835, 0.8627239955489192] | [0.9894034768309156, 0.9120129014770962] |
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+ | 0.0137 | 23.0 | 1978 | 0.1701 | 0.9136 | 0.9484 | 0.9760 | [0.9719681843338751, 0.8552607882028388] | [0.9885083690609032, 0.908250815050119] |
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+ | 0.0142 | 24.0 | 2064 | 0.1646 | 0.9146 | 0.9488 | 0.9763 | [0.9723134197764093, 0.8568918401744342] | [0.9887405884771245, 0.9089100747034281] |
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+ | 0.0156 | 25.0 | 2150 | 0.1615 | 0.9144 | 0.9465 | 0.9763 | [0.9723929259786395, 0.856345354289624] | [0.9898487696012216, 0.9032139066422469] |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 2.0.1
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3