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--- |
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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 |
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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-b1-finetuned-segments-pv_v1_x3_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/ktaai3s5) |
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# segformer-b1-finetuned-segments-pv_v1_x3_normalized_p100_4batch |
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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: 0.0064 |
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- Mean Iou: 0.8466 |
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- Precision: 0.9220 |
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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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| 0.0084 | 0.9993 | 687 | 0.0063 | 0.8160 | 0.8736 | |
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| 0.007 | 2.0 | 1375 | 0.0060 | 0.8262 | 0.9006 | |
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| 0.006 | 2.9993 | 2062 | 0.0066 | 0.8072 | 0.9214 | |
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| 0.0049 | 4.0 | 2750 | 0.0054 | 0.8283 | 0.9287 | |
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| 0.004 | 4.9993 | 3437 | 0.0070 | 0.8326 | 0.9068 | |
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| 0.0042 | 6.0 | 4125 | 0.0053 | 0.8318 | 0.8834 | |
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| 0.004 | 6.9993 | 4812 | 0.0053 | 0.8370 | 0.8893 | |
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| 0.0037 | 8.0 | 5500 | 0.0075 | 0.8049 | 0.9404 | |
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| 0.0036 | 8.9993 | 6187 | 0.0074 | 0.8222 | 0.9106 | |
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| 0.0033 | 10.0 | 6875 | 0.0061 | 0.8297 | 0.9161 | |
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| 0.0031 | 10.9993 | 7562 | 0.0055 | 0.8427 | 0.9086 | |
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| 0.0033 | 12.0 | 8250 | 0.0052 | 0.8437 | 0.9152 | |
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| 0.0037 | 12.9993 | 8937 | 0.0055 | 0.8387 | 0.9186 | |
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| 0.0028 | 14.0 | 9625 | 0.0060 | 0.8416 | 0.9137 | |
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| 0.0027 | 14.9993 | 10312 | 0.0052 | 0.8489 | 0.9212 | |
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| 0.003 | 16.0 | 11000 | 0.0065 | 0.8393 | 0.9158 | |
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| 0.0025 | 16.9993 | 11687 | 0.0063 | 0.8347 | 0.9245 | |
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| 0.0027 | 18.0 | 12375 | 0.0065 | 0.8439 | 0.9093 | |
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| 0.0032 | 18.9993 | 13062 | 0.0056 | 0.8495 | 0.9186 | |
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| 0.0024 | 20.0 | 13750 | 0.0064 | 0.8466 | 0.9220 | |
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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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