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--- |
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license: other |
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base_model: nvidia/segformer-b2-finetuned-ade-512-512 |
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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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metrics: |
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- precision |
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model-index: |
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- name: segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch |
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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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[<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/mmtwbyor) |
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# segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch |
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This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b2-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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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: 0.0004 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.001 |
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- num_epochs: 40 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:| |
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| 0.01 | 0.9989 | 229 | 0.0088 | 0.8105 | 0.8817 | |
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| 0.0062 | 1.9978 | 458 | 0.0075 | 0.8201 | 0.8726 | |
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| 0.0049 | 2.9967 | 687 | 0.0063 | 0.8297 | 0.8867 | |
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| 0.0053 | 4.0 | 917 | 0.0055 | 0.8425 | 0.8845 | |
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| 0.0037 | 4.9989 | 1146 | 0.0058 | 0.8380 | 0.8823 | |
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| 0.0039 | 5.9978 | 1375 | 0.0211 | 0.6114 | 0.9766 | |
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| 0.0037 | 6.9967 | 1604 | 0.3403 | 0.0 | 1.0 | |
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| 0.0002 | 8.0 | 1834 | nan | 0.0 | 1.0 | |
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| 0.0003 | 8.9989 | 2063 | nan | 0.0 | 1.0 | |
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| 0.0864 | 9.9978 | 2292 | nan | 0.0 | 1.0 | |
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| 0.0035 | 10.9967 | 2521 | nan | 0.0 | 1.0 | |
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| 0.0045 | 12.0 | 2751 | nan | 0.0 | 1.0 | |
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| 0.0039 | 12.9989 | 2980 | nan | 0.0 | 1.0 | |
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| 0.8023 | 13.9978 | 3209 | nan | 0.0 | 1.0 | |
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| 0.0041 | 14.9967 | 3438 | nan | 0.0 | 1.0 | |
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| 7.0711 | 16.0 | 3668 | nan | 0.0 | 1.0 | |
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| 0.0039 | 16.9989 | 3897 | nan | 0.0 | 1.0 | |
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| 19.4385 | 17.9978 | 4126 | nan | 0.0 | 1.0 | |
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| 0.0001 | 18.9967 | 4355 | nan | 0.0 | 1.0 | |
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| 1.7398 | 20.0 | 4585 | nan | 0.0 | 1.0 | |
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| 0.2879 | 20.9989 | 4814 | nan | 0.0 | 1.0 | |
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| 0.0005 | 21.9978 | 5043 | nan | 0.0 | 1.0 | |
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| 5.8398 | 22.9967 | 5272 | nan | 0.0 | 1.0 | |
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| 0.0004 | 24.0 | 5502 | nan | 0.0 | 1.0 | |
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| 0.0002 | 24.9989 | 5731 | nan | 0.0 | 1.0 | |
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| 0.0016 | 25.9978 | 5960 | nan | 0.0 | 1.0 | |
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| 0.0034 | 26.9967 | 6189 | nan | 0.0 | 1.0 | |
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| 0.0004 | 28.0 | 6419 | nan | 0.0 | 1.0 | |
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| 0.0036 | 28.9989 | 6648 | nan | 0.0 | 1.0 | |
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| 0.0314 | 29.9978 | 6877 | nan | 0.0 | 1.0 | |
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| 0.0921 | 30.9967 | 7106 | nan | 0.0 | 1.0 | |
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| 89.1025 | 32.0 | 7336 | nan | 0.0 | 1.0 | |
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| 0.0073 | 32.9989 | 7565 | nan | 0.0 | 1.0 | |
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| 0.0126 | 33.9978 | 7794 | nan | 0.0 | 1.0 | |
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| 0.0094 | 34.9967 | 8023 | nan | 0.0 | 1.0 | |
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| 0.0001 | 36.0 | 8253 | nan | 0.0 | 1.0 | |
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| 4.3987 | 36.9989 | 8482 | nan | 0.0 | 1.0 | |
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| 0.0005 | 37.9978 | 8711 | nan | 0.0 | 1.0 | |
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| 0.0202 | 38.9967 | 8940 | nan | 0.0 | 1.0 | |
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| 0.1612 | 39.9564 | 9160 | nan | 0.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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