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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: segformer-b0-finetuned-agriculture-freeze-encoder |
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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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# segformer-b0-finetuned-agriculture-freeze-encoder |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4279 |
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- Mean Iou: 0.2863 |
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- Mean Accuracy: 0.3449 |
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- Overall Accuracy: 0.3991 |
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- Accuracy Unlabeled: nan |
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- Accuracy Nutrient Deficiency: 0.3911 |
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- Accuracy Planter Skip: 0.2441 |
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- Accuracy Water: 0.7100 |
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- Accuracy Waterway: 0.1217 |
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- Accuracy Weed Cluster: 0.2574 |
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- Iou Unlabeled: 0.0 |
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- Iou Nutrient Deficiency: 0.3885 |
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- Iou Planter Skip: 0.2436 |
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- Iou Water: 0.7074 |
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- Iou Waterway: 0.1213 |
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- Iou Weed Cluster: 0.2569 |
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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: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Nutrient Deficiency | Accuracy Planter Skip | Accuracy Water | Accuracy Waterway | Accuracy Weed Cluster | Iou Unlabeled | Iou Nutrient Deficiency | Iou Planter Skip | Iou Water | Iou Waterway | Iou Weed Cluster | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------------------:|:---------------------:|:--------------:|:-----------------:|:---------------------:|:-------------:|:-----------------------:|:----------------:|:---------:|:------------:|:----------------:| |
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| 0.2023 | 1.0 | 8145 | 0.5192 | 0.1103 | 0.1327 | 0.1902 | nan | 0.2070 | 0.0003 | 0.3457 | 0.0000 | 0.1105 | 0.0 | 0.2057 | 0.0003 | 0.3455 | 0.0000 | 0.1103 | |
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| 0.7172 | 2.0 | 16290 | 0.4974 | 0.1282 | 0.1543 | 0.2138 | nan | 0.2617 | 0.0332 | 0.3582 | 0.0098 | 0.1083 | 0.0 | 0.2601 | 0.0332 | 0.3582 | 0.0098 | 0.1082 | |
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| 0.6844 | 3.0 | 24435 | 0.4657 | 0.2032 | 0.2445 | 0.3092 | nan | 0.3512 | 0.1223 | 0.5564 | 0.0384 | 0.1544 | 0.0 | 0.3492 | 0.1220 | 0.5554 | 0.0382 | 0.1543 | |
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| 0.2052 | 4.0 | 32580 | 0.4671 | 0.1912 | 0.2299 | 0.2961 | nan | 0.3261 | 0.1340 | 0.4389 | 0.0347 | 0.2160 | 0.0 | 0.3245 | 0.1338 | 0.4389 | 0.0346 | 0.2154 | |
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| 0.6564 | 5.0 | 40725 | 0.4468 | 0.2317 | 0.2788 | 0.3460 | nan | 0.3663 | 0.1487 | 0.5721 | 0.0793 | 0.2278 | 0.0 | 0.3642 | 0.1485 | 0.5715 | 0.0790 | 0.2272 | |
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| 0.1997 | 6.0 | 48870 | 0.4483 | 0.2392 | 0.2879 | 0.3446 | nan | 0.3524 | 0.1821 | 0.6219 | 0.0772 | 0.2059 | 0.0 | 0.3501 | 0.1817 | 0.6209 | 0.0769 | 0.2055 | |
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| 0.3586 | 7.0 | 57015 | 0.4413 | 0.2379 | 0.2860 | 0.3492 | nan | 0.3676 | 0.2073 | 0.5656 | 0.0505 | 0.2392 | 0.0 | 0.3661 | 0.2069 | 0.5653 | 0.0504 | 0.2387 | |
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| 0.7879 | 8.0 | 65160 | 0.4369 | 0.2501 | 0.3008 | 0.3597 | nan | 0.3632 | 0.2320 | 0.6003 | 0.0585 | 0.2497 | 0.0 | 0.3618 | 0.2315 | 0.6000 | 0.0584 | 0.2491 | |
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| 1.137 | 9.0 | 73305 | 0.4393 | 0.2649 | 0.3189 | 0.3853 | nan | 0.4236 | 0.2202 | 0.6417 | 0.0772 | 0.2316 | 0.0 | 0.4212 | 0.2197 | 0.6405 | 0.0770 | 0.2312 | |
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| 0.2625 | 10.0 | 81450 | 0.4388 | 0.2540 | 0.3057 | 0.3850 | nan | 0.4493 | 0.2058 | 0.5276 | 0.0705 | 0.2755 | 0.0 | 0.4460 | 0.2054 | 0.5275 | 0.0704 | 0.2748 | |
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| 0.2108 | 11.0 | 89595 | 0.4308 | 0.2845 | 0.3427 | 0.4149 | nan | 0.4475 | 0.2446 | 0.6482 | 0.0891 | 0.2838 | 0.0 | 0.4438 | 0.2440 | 0.6472 | 0.0889 | 0.2830 | |
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| 0.3237 | 12.0 | 97740 | 0.4251 | 0.2858 | 0.3440 | 0.4225 | nan | 0.4322 | 0.2372 | 0.6314 | 0.0854 | 0.3336 | 0.0 | 0.4296 | 0.2367 | 0.6309 | 0.0853 | 0.3322 | |
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| 1.0289 | 13.0 | 105885 | 0.4488 | 0.2604 | 0.3138 | 0.3823 | nan | 0.4623 | 0.2111 | 0.6343 | 0.0744 | 0.1869 | 0.0 | 0.4575 | 0.2107 | 0.6332 | 0.0742 | 0.1867 | |
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| 0.6843 | 14.0 | 114030 | 0.4253 | 0.2922 | 0.3519 | 0.4252 | nan | 0.4515 | 0.2267 | 0.6901 | 0.1089 | 0.2824 | 0.0 | 0.4481 | 0.2263 | 0.6884 | 0.1086 | 0.2815 | |
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| 0.2695 | 15.0 | 122175 | 0.4299 | 0.2856 | 0.3437 | 0.3878 | nan | 0.3812 | 0.2818 | 0.6873 | 0.1207 | 0.2472 | 0.0 | 0.3801 | 0.2811 | 0.6858 | 0.1203 | 0.2466 | |
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| 0.3991 | 16.0 | 130320 | 0.4225 | 0.2938 | 0.3534 | 0.4137 | nan | 0.4213 | 0.2714 | 0.6712 | 0.1131 | 0.2898 | 0.0 | 0.4198 | 0.2708 | 0.6702 | 0.1129 | 0.2888 | |
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| 0.7352 | 17.0 | 138465 | 0.4303 | 0.2732 | 0.3288 | 0.3894 | nan | 0.4176 | 0.2558 | 0.5941 | 0.1024 | 0.2740 | 0.0 | 0.4150 | 0.2553 | 0.5939 | 0.1022 | 0.2731 | |
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| 0.6884 | 18.0 | 146610 | 0.4243 | 0.2956 | 0.3556 | 0.4135 | nan | 0.4154 | 0.2735 | 0.6575 | 0.1294 | 0.3024 | 0.0 | 0.4137 | 0.2728 | 0.6568 | 0.1290 | 0.3013 | |
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| 0.3863 | 19.0 | 154755 | 0.4249 | 0.2861 | 0.3445 | 0.4184 | nan | 0.4597 | 0.2254 | 0.6370 | 0.1138 | 0.2864 | 0.0 | 0.4561 | 0.2251 | 0.6365 | 0.1135 | 0.2858 | |
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| 0.3208 | 20.0 | 162900 | 0.4279 | 0.2863 | 0.3449 | 0.3991 | nan | 0.3911 | 0.2441 | 0.7100 | 0.1217 | 0.2574 | 0.0 | 0.3885 | 0.2436 | 0.7074 | 0.1213 | 0.2569 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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