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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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base_model: nvidia/mit-b0 |
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model-index: |
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- name: Aerial-Drone-Image-Segmentation |
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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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# Aerial-Drone-Image-Segmentation |
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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: 0.8852 |
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- Mean Iou: 0.2994 |
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- Mean Accuracy: 0.3923 |
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- Overall Accuracy: 0.7774 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 24 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:| |
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| 2.7923 | 1.25 | 20 | 2.8338 | 0.0954 | 0.1626 | 0.5529 | |
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| 2.219 | 2.5 | 40 | 2.1391 | 0.1036 | 0.1666 | 0.5929 | |
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| 1.9451 | 3.75 | 60 | 1.7919 | 0.1154 | 0.1782 | 0.6129 | |
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| 1.7558 | 5.0 | 80 | 1.6767 | 0.1300 | 0.1961 | 0.6396 | |
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| 1.6381 | 6.25 | 100 | 1.5817 | 0.1383 | 0.2055 | 0.6550 | |
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| 1.5338 | 7.5 | 120 | 1.4816 | 0.1464 | 0.2140 | 0.6729 | |
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| 1.4478 | 8.75 | 140 | 1.4231 | 0.1529 | 0.2219 | 0.6823 | |
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| 1.361 | 10.0 | 160 | 1.3300 | 0.1637 | 0.2315 | 0.6975 | |
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| 1.306 | 11.25 | 180 | 1.3034 | 0.1737 | 0.2419 | 0.7060 | |
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| 1.2611 | 12.5 | 200 | 1.2692 | 0.1755 | 0.2450 | 0.7093 | |
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| 1.2317 | 13.75 | 220 | 1.2190 | 0.1821 | 0.2501 | 0.7145 | |
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| 1.1868 | 15.0 | 240 | 1.2063 | 0.1862 | 0.2539 | 0.7188 | |
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| 1.1628 | 16.25 | 260 | 1.1832 | 0.1909 | 0.2612 | 0.7234 | |
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| 1.1149 | 17.5 | 280 | 1.1368 | 0.2048 | 0.2739 | 0.7317 | |
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| 1.1009 | 18.75 | 300 | 1.1117 | 0.2232 | 0.2938 | 0.7387 | |
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| 1.0532 | 20.0 | 320 | 1.0923 | 0.2315 | 0.2997 | 0.7414 | |
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| 1.0464 | 21.25 | 340 | 1.0821 | 0.2408 | 0.3147 | 0.7480 | |
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| 1.0278 | 22.5 | 360 | 1.0541 | 0.2517 | 0.3277 | 0.7530 | |
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| 0.9945 | 23.75 | 380 | 1.0352 | 0.2612 | 0.3398 | 0.7573 | |
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| 0.9729 | 25.0 | 400 | 1.0207 | 0.2671 | 0.3511 | 0.7609 | |
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| 0.9527 | 26.25 | 420 | 1.0067 | 0.2684 | 0.3547 | 0.7609 | |
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| 0.9494 | 27.5 | 440 | 0.9870 | 0.2713 | 0.3548 | 0.7627 | |
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| 0.9287 | 28.75 | 460 | 0.9729 | 0.2745 | 0.3619 | 0.7640 | |
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| 0.9089 | 30.0 | 480 | 0.9561 | 0.2791 | 0.3640 | 0.7680 | |
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| 0.9064 | 31.25 | 500 | 0.9500 | 0.2799 | 0.3712 | 0.7672 | |
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| 0.8681 | 32.5 | 520 | 0.9397 | 0.2845 | 0.3749 | 0.7696 | |
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| 0.8677 | 33.75 | 540 | 0.9340 | 0.2835 | 0.3737 | 0.7692 | |
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| 0.8663 | 35.0 | 560 | 0.9243 | 0.2862 | 0.3755 | 0.7716 | |
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| 0.8629 | 36.25 | 580 | 0.9173 | 0.2869 | 0.3766 | 0.7719 | |
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| 0.8542 | 37.5 | 600 | 0.9112 | 0.2908 | 0.3810 | 0.7740 | |
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| 0.8391 | 38.75 | 620 | 0.9050 | 0.2904 | 0.3812 | 0.7734 | |
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| 0.8392 | 40.0 | 640 | 0.9027 | 0.2917 | 0.3818 | 0.7734 | |
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| 0.8306 | 41.25 | 660 | 0.8949 | 0.2941 | 0.3841 | 0.7755 | |
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| 0.8213 | 42.5 | 680 | 0.8936 | 0.2958 | 0.3875 | 0.7760 | |
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| 0.8406 | 43.75 | 700 | 0.8910 | 0.2964 | 0.3879 | 0.7763 | |
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| 0.8254 | 45.0 | 720 | 0.8889 | 0.2981 | 0.3897 | 0.7764 | |
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| 0.8202 | 46.25 | 740 | 0.8880 | 0.2985 | 0.3917 | 0.7767 | |
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| 0.8013 | 47.5 | 760 | 0.8891 | 0.2989 | 0.3923 | 0.7767 | |
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| 0.8188 | 48.75 | 780 | 0.8861 | 0.2994 | 0.3926 | 0.7772 | |
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| 0.8089 | 50.0 | 800 | 0.8852 | 0.2994 | 0.3923 | 0.7774 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.2 |
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