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segformer-b1-finetuned-segments-pv_v1_normalized_t4_16batch

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  1. README.md +57 -57
  2. model.safetensors +1 -1
  3. runs/Jul26_10-22-18_e9717352b340/events.out.tfevents.1721989344.e9717352b340.25.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -1,12 +1,12 @@
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  ---
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- base_model: nvidia/segformer-b1-finetuned-ade-512-512
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  license: other
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- metrics:
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- - precision
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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-b1-finetuned-segments-pv_v1_normalized_t4_16batch
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  results: []
@@ -15,14 +15,14 @@ model-index:
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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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- [<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/7zdji4yh)
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  # segformer-b1-finetuned-segments-pv_v1_normalized_t4_16batch
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0082
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- - Mean Iou: 0.8638
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- - Precision: 0.9180
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  ## Model description
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@@ -41,7 +41,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0016
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
@@ -56,55 +56,55 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
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- | 0.0089 | 0.9739 | 28 | 0.0081 | 0.7635 | 0.8665 |
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- | 0.0087 | 1.9826 | 57 | 0.0098 | 0.7340 | 0.7592 |
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- | 0.0053 | 2.9913 | 86 | 0.0056 | 0.8284 | 0.9168 |
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- | 0.0073 | 4.0 | 115 | 0.0069 | 0.7988 | 0.8608 |
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- | 0.005 | 4.9739 | 143 | 0.0059 | 0.8186 | 0.8704 |
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- | 0.0042 | 5.9826 | 172 | 0.0059 | 0.8262 | 0.8836 |
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- | 0.0047 | 6.9913 | 201 | 0.0058 | 0.8225 | 0.8749 |
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- | 0.0044 | 8.0 | 230 | 0.0060 | 0.8168 | 0.8677 |
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- | 0.0041 | 8.9739 | 258 | 0.0070 | 0.8057 | 0.8494 |
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- | 0.0031 | 9.9826 | 287 | 0.0057 | 0.8260 | 0.8832 |
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- | 0.0057 | 10.9913 | 316 | 0.0058 | 0.8289 | 0.8744 |
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- | 0.0039 | 12.0 | 345 | 0.0065 | 0.8190 | 0.8625 |
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- | 0.0033 | 12.9739 | 373 | 0.0061 | 0.8316 | 0.9113 |
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- | 0.0031 | 13.9826 | 402 | 0.0066 | 0.8329 | 0.8947 |
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- | 0.0036 | 14.9913 | 431 | 0.0059 | 0.8392 | 0.8996 |
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- | 0.0034 | 16.0 | 460 | 0.0060 | 0.8379 | 0.8977 |
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- | 0.004 | 16.9739 | 488 | 0.0064 | 0.8434 | 0.9075 |
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- | 0.0029 | 17.9826 | 517 | 0.0060 | 0.8450 | 0.9010 |
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- | 0.0027 | 18.9913 | 546 | 0.0059 | 0.8431 | 0.9038 |
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- | 0.0035 | 20.0 | 575 | 0.0060 | 0.8445 | 0.9049 |
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- | 0.0028 | 20.9739 | 603 | 0.0062 | 0.8474 | 0.9320 |
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- | 0.0024 | 21.9826 | 632 | 0.0063 | 0.8442 | 0.8984 |
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- | 0.0021 | 22.9913 | 661 | 0.0059 | 0.8526 | 0.9030 |
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- | 0.0021 | 24.0 | 690 | 0.0059 | 0.8546 | 0.9126 |
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- | 0.0026 | 24.9739 | 718 | 0.0065 | 0.8542 | 0.9110 |
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- | 0.0024 | 25.9826 | 747 | 0.0068 | 0.8451 | 0.8921 |
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- | 0.0019 | 26.9913 | 776 | 0.0069 | 0.8462 | 0.9029 |
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- | 0.002 | 28.0 | 805 | 0.0071 | 0.8522 | 0.9145 |
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- | 0.0022 | 28.9739 | 833 | 0.0077 | 0.8395 | 0.9304 |
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- | 0.0019 | 29.9826 | 862 | 0.0069 | 0.8567 | 0.9167 |
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- | 0.0027 | 30.9913 | 891 | 0.0073 | 0.8478 | 0.8957 |
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- | 0.0026 | 32.0 | 920 | 0.0069 | 0.8575 | 0.8994 |
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- | 0.0017 | 32.9739 | 948 | 0.0065 | 0.8602 | 0.9098 |
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- | 0.0023 | 33.9826 | 977 | 0.0071 | 0.8517 | 0.8915 |
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- | 0.0019 | 34.9913 | 1006 | 0.0069 | 0.8629 | 0.9200 |
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- | 0.0016 | 36.0 | 1035 | 0.0064 | 0.8684 | 0.9213 |
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- | 0.0017 | 36.9739 | 1063 | 0.0068 | 0.8612 | 0.9147 |
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- | 0.002 | 37.9826 | 1092 | 0.0074 | 0.8608 | 0.9295 |
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- | 0.0014 | 38.9913 | 1121 | 0.0069 | 0.8660 | 0.9222 |
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- | 0.0014 | 40.0 | 1150 | 0.0075 | 0.8624 | 0.9173 |
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- | 0.0013 | 40.9739 | 1178 | 0.0076 | 0.8560 | 0.9105 |
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- | 0.0013 | 41.9826 | 1207 | 0.0076 | 0.8640 | 0.9193 |
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- | 0.0013 | 42.9913 | 1236 | 0.0076 | 0.8625 | 0.9078 |
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- | 0.0012 | 44.0 | 1265 | 0.0078 | 0.8647 | 0.9152 |
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- | 0.0013 | 44.9739 | 1293 | 0.0078 | 0.8652 | 0.9176 |
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- | 0.0012 | 45.9826 | 1322 | 0.0078 | 0.8638 | 0.9209 |
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- | 0.0012 | 46.9913 | 1351 | 0.0081 | 0.8639 | 0.9186 |
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- | 0.001 | 48.0 | 1380 | 0.0079 | 0.8641 | 0.9181 |
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- | 0.0011 | 48.6957 | 1400 | 0.0082 | 0.8638 | 0.9180 |
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  ### Framework versions
 
1
  ---
 
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  license: other
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+ base_model: nvidia/segformer-b1-finetuned-ade-512-512
 
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  tags:
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  - vision
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  - image-segmentation
7
  - generated_from_trainer
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+ metrics:
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+ - precision
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  model-index:
11
  - name: segformer-b1-finetuned-segments-pv_v1_normalized_t4_16batch
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  results: []
 
15
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
16
  should probably proofread and complete it, then remove this comment. -->
17
 
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+ [<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/thj03afd)
19
  # segformer-b1-finetuned-segments-pv_v1_normalized_t4_16batch
20
 
21
  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: nan
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+ - Mean Iou: 0.0
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+ - Precision: 1.0
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  ## Model description
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  ### Training hyperparameters
42
 
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0032
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|
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+ | 0.024 | 0.9739 | 28 | 0.0208 | 0.0 | 1.0 |
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+ | 0.0156 | 1.9826 | 57 | 0.0145 | 0.6782 | 0.8283 |
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+ | 0.009 | 2.9913 | 86 | 0.0115 | 0.7282 | 0.9124 |
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+ | 0.0132 | 4.0 | 115 | 0.0098 | 0.7516 | 0.8954 |
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+ | 0.0069 | 4.9739 | 143 | 0.0082 | 0.7743 | 0.9003 |
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+ | 0.0058 | 5.9826 | 172 | 0.0092 | 0.7860 | 0.9163 |
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+ | 0.0064 | 6.9913 | 201 | 0.0115 | 0.7790 | 0.9320 |
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+ | 0.0049 | 8.0 | 230 | 0.0627 | 0.5688 | 0.9780 |
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+ | 0.0063 | 8.9739 | 258 | 0.1204 | 0.3259 | 0.9889 |
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+ | 0.0055 | 9.9826 | 287 | 0.2607 | 0.0005 | 1.0 |
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+ | 0.0065 | 10.9913 | 316 | 0.3377 | 0.0 | 1.0 |
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+ | 0.0079 | 12.0 | 345 | 0.6538 | 0.0 | 1.0 |
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+ | 0.0 | 12.9739 | 373 | nan | 0.0 | 1.0 |
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+ | 0.0 | 13.9826 | 402 | nan | 0.0 | 1.0 |
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+ | 0.0 | 14.9913 | 431 | nan | 0.0 | 1.0 |
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+ | 0.0 | 16.0 | 460 | nan | 0.0 | 1.0 |
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+ | 0.0 | 16.9739 | 488 | nan | 0.0 | 1.0 |
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+ | 0.0 | 17.9826 | 517 | nan | 0.0 | 1.0 |
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+ | 0.0 | 18.9913 | 546 | nan | 0.0 | 1.0 |
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+ | 0.0 | 20.0 | 575 | nan | 0.0 | 1.0 |
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+ | 0.0 | 20.9739 | 603 | nan | 0.0 | 1.0 |
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+ | 0.0 | 21.9826 | 632 | nan | 0.0 | 1.0 |
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+ | 0.0 | 22.9913 | 661 | nan | 0.0 | 1.0 |
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+ | 0.0 | 24.0 | 690 | nan | 0.0 | 1.0 |
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+ | 0.0 | 24.9739 | 718 | nan | 0.0 | 1.0 |
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+ | 0.0 | 25.9826 | 747 | nan | 0.0 | 1.0 |
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+ | 0.0 | 26.9913 | 776 | nan | 0.0 | 1.0 |
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+ | 0.0 | 28.0 | 805 | nan | 0.0 | 1.0 |
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+ | 0.0 | 28.9739 | 833 | nan | 0.0 | 1.0 |
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+ | 0.0 | 29.9826 | 862 | nan | 0.0 | 1.0 |
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+ | 0.0 | 30.9913 | 891 | nan | 0.0 | 1.0 |
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+ | 0.0 | 32.0 | 920 | nan | 0.0 | 1.0 |
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+ | 0.0 | 32.9739 | 948 | nan | 0.0 | 1.0 |
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+ | 0.0 | 33.9826 | 977 | nan | 0.0 | 1.0 |
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+ | 0.0 | 34.9913 | 1006 | nan | 0.0 | 1.0 |
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+ | 0.0 | 36.0 | 1035 | nan | 0.0 | 1.0 |
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+ | 0.0 | 36.9739 | 1063 | nan | 0.0 | 1.0 |
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+ | 0.0 | 37.9826 | 1092 | nan | 0.0 | 1.0 |
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+ | 0.0 | 38.9913 | 1121 | nan | 0.0 | 1.0 |
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+ | 0.0 | 40.0 | 1150 | nan | 0.0 | 1.0 |
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+ | 0.0 | 40.9739 | 1178 | nan | 0.0 | 1.0 |
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+ | 0.0 | 41.9826 | 1207 | nan | 0.0 | 1.0 |
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+ | 0.0 | 42.9913 | 1236 | nan | 0.0 | 1.0 |
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+ | 0.0 | 44.0 | 1265 | nan | 0.0 | 1.0 |
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+ | 0.0 | 44.9739 | 1293 | nan | 0.0 | 1.0 |
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+ | 0.0 | 45.9826 | 1322 | nan | 0.0 | 1.0 |
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+ | 0.0 | 46.9913 | 1351 | nan | 0.0 | 1.0 |
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+ | 0.0 | 48.0 | 1380 | nan | 0.0 | 1.0 |
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+ | 0.0 | 48.6957 | 1400 | nan | 0.0 | 1.0 |
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  ### Framework versions
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