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--- |
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license: other |
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base_model: nvidia/segformer-b0-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-b0-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/hwghoj9l) |
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# segformer-b0-finetuned-segments-pv_v1_x3_normalized_p100_4batch |
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This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-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: 0.0056 |
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- Mean Iou: 0.8288 |
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- Precision: 0.8928 |
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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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| 0.0086 | 0.9993 | 687 | 0.0068 | 0.8080 | 0.8515 | |
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| 0.0061 | 2.0 | 1375 | 0.0056 | 0.8257 | 0.8862 | |
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| 0.0058 | 2.9993 | 2062 | 0.0056 | 0.8284 | 0.9154 | |
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| 0.0063 | 4.0 | 2750 | 0.0055 | 0.8212 | 0.9261 | |
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| 0.0051 | 4.9993 | 3437 | 0.0081 | 0.7851 | 0.9189 | |
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| 0.0042 | 6.0 | 4125 | 0.0062 | 0.8322 | 0.9034 | |
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| 0.004 | 6.9993 | 4812 | 0.0067 | 0.8262 | 0.8807 | |
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| 0.0049 | 8.0 | 5500 | 0.0061 | 0.8271 | 0.9135 | |
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| 0.0043 | 8.9993 | 6187 | 0.0056 | 0.8288 | 0.8928 | |
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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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