File size: 4,949 Bytes
0177db3 ddb6290 0177db3 ddb6290 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
---
license: other
base_model: nvidia/segformer-b1-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
metrics:
- precision
model-index:
- name: segformer-b1-finetuned-segments-pv_v1_normalized_p100_4batch_fp
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mouadn773/huggingface/runs/kxwmffd1)
# segformer-b1-finetuned-segments-pv_v1_normalized_p100_4batch_fp
This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b1-finetuned-ade-512-512) on the mouadenna/satellite_PV_dataset_train_test_v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Mean Iou: 0.9589
- Precision: 0.9794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.001
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
| 0.0641 | 0.9989 | 229 | 0.0082 | 0.8288 | 0.8881 |
| 0.0077 | 1.9978 | 458 | 0.0070 | 0.8228 | 0.8650 |
| 0.0058 | 2.9967 | 687 | 0.0042 | 0.8827 | 0.9339 |
| 0.005 | 4.0 | 917 | 0.0039 | 0.8849 | 0.9172 |
| 0.0044 | 4.9989 | 1146 | 0.0071 | 0.7938 | 0.8122 |
| 0.0049 | 5.9978 | 1375 | 0.0036 | 0.8914 | 0.9402 |
| 0.0045 | 6.9967 | 1604 | 0.0042 | 0.8729 | 0.9280 |
| 0.0038 | 8.0 | 1834 | 0.0035 | 0.8889 | 0.9433 |
| 0.0034 | 8.9989 | 2063 | 0.0030 | 0.9038 | 0.9357 |
| 0.0032 | 9.9978 | 2292 | 0.0026 | 0.9115 | 0.9501 |
| 0.003 | 10.9967 | 2521 | 0.0026 | 0.9136 | 0.9482 |
| 0.0031 | 12.0 | 2751 | 0.0026 | 0.9132 | 0.9461 |
| 0.0029 | 12.9989 | 2980 | 0.0026 | 0.9144 | 0.9493 |
| 0.0026 | 13.9978 | 3209 | 0.0023 | 0.9202 | 0.9414 |
| 0.0025 | 14.9967 | 3438 | 0.0024 | 0.9175 | 0.9456 |
| 0.003 | 16.0 | 3668 | 0.0032 | 0.8926 | 0.9640 |
| 0.0035 | 16.9989 | 3897 | 0.0041 | 0.8741 | 0.9007 |
| 0.0029 | 17.9978 | 4126 | 0.0022 | 0.9229 | 0.9598 |
| 0.0024 | 18.9967 | 4355 | 0.0022 | 0.9239 | 0.9549 |
| 0.0022 | 20.0 | 4585 | 0.0020 | 0.9308 | 0.9601 |
| 0.0021 | 20.9989 | 4814 | 0.0019 | 0.9325 | 0.9689 |
| 0.0021 | 21.9978 | 5043 | 0.0019 | 0.9334 | 0.9630 |
| 0.002 | 22.9967 | 5272 | 0.0018 | 0.9368 | 0.9631 |
| 0.002 | 24.0 | 5502 | 0.0019 | 0.9333 | 0.9684 |
| 0.002 | 24.9989 | 5731 | 0.0018 | 0.9381 | 0.9613 |
| 0.0022 | 25.9978 | 5960 | 0.0018 | 0.9369 | 0.9610 |
| 0.0019 | 26.9967 | 6189 | 0.0017 | 0.9413 | 0.9677 |
| 0.0018 | 28.0 | 6419 | 0.0016 | 0.9429 | 0.9629 |
| 0.0017 | 28.9989 | 6648 | 0.0016 | 0.9444 | 0.9642 |
| 0.0017 | 29.9978 | 6877 | 0.0015 | 0.9465 | 0.9741 |
| 0.0016 | 30.9967 | 7106 | 0.0014 | 0.9492 | 0.9718 |
| 0.0016 | 32.0 | 7336 | 0.0014 | 0.9499 | 0.9687 |
| 0.0015 | 32.9989 | 7565 | 0.0015 | 0.9469 | 0.9737 |
| 0.0016 | 33.9978 | 7794 | 0.0014 | 0.9514 | 0.9721 |
| 0.0015 | 34.9967 | 8023 | 0.0013 | 0.9542 | 0.9719 |
| 0.0014 | 36.0 | 8253 | 0.0013 | 0.9546 | 0.9694 |
| 0.0014 | 36.9989 | 8482 | 0.0012 | 0.9569 | 0.9740 |
| 0.0014 | 37.9978 | 8711 | 0.0012 | 0.9579 | 0.9781 |
| 0.0014 | 38.9967 | 8940 | 0.0012 | 0.9584 | 0.9759 |
| 0.0013 | 39.9564 | 9160 | 0.0012 | 0.9589 | 0.9794 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
|