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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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- depth-estimation |
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- generated_from_trainer |
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model-index: |
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- name: glpn-nyu-finetuned-diode-230530-204740 |
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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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# glpn-nyu-finetuned-diode-230530-204740 |
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This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5139 |
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- Mae: 3.0509 |
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- Rmse: 3.4756 |
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- Abs Rel: 5.7613 |
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- Log Mae: 0.6836 |
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- Log Rmse: 0.8048 |
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- Delta1: 0.3028 |
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- Delta2: 0.3079 |
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- Delta3: 0.3096 |
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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: 1e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 48 |
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- seed: 2022 |
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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: 0.1 |
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- num_epochs: 10 |
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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 | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| |
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| No log | 1.0 | 1 | 1.5335 | 3.1427 | 3.6089 | 5.9847 | 0.6920 | 0.8173 | 0.3016 | 0.3077 | 0.3094 | |
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| No log | 2.0 | 2 | 1.5297 | 3.1246 | 3.5833 | 5.9419 | 0.6903 | 0.8149 | 0.3018 | 0.3077 | 0.3094 | |
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| No log | 3.0 | 3 | 1.5263 | 3.1085 | 3.5602 | 5.9033 | 0.6889 | 0.8128 | 0.3020 | 0.3078 | 0.3095 | |
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| No log | 4.0 | 4 | 1.5234 | 3.0947 | 3.5400 | 5.8694 | 0.6876 | 0.8109 | 0.3022 | 0.3078 | 0.3095 | |
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| No log | 5.0 | 5 | 1.5208 | 3.0825 | 3.5222 | 5.8395 | 0.6865 | 0.8092 | 0.3024 | 0.3079 | 0.3095 | |
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| No log | 6.0 | 6 | 1.5185 | 3.0723 | 3.5072 | 5.8144 | 0.6856 | 0.8078 | 0.3025 | 0.3079 | 0.3095 | |
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| No log | 7.0 | 7 | 1.5167 | 3.0639 | 3.4949 | 5.7937 | 0.6848 | 0.8067 | 0.3026 | 0.3079 | 0.3096 | |
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| No log | 8.0 | 8 | 1.5153 | 3.0574 | 3.4852 | 5.7775 | 0.6842 | 0.8057 | 0.3027 | 0.3079 | 0.3096 | |
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| No log | 9.0 | 9 | 1.5143 | 3.0531 | 3.4788 | 5.7667 | 0.6838 | 0.8051 | 0.3028 | 0.3079 | 0.3096 | |
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| No log | 10.0 | 10 | 1.5139 | 3.0509 | 3.4756 | 5.7613 | 0.6836 | 0.8048 | 0.3028 | 0.3079 | 0.3096 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Tokenizers 0.13.3 |
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