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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-230530-193901 |
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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-230530-193901 |
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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: 1.5356 |
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- Mae: 3.1497 |
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- Rmse: 3.6237 |
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- Abs Rel: 6.0096 |
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- Log Mae: 0.6926 |
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- Log Rmse: 0.8186 |
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- Delta1: 0.3020 |
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- Delta2: 0.3077 |
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- Delta3: 0.3094 |
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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 | 1.5604 | 3.2768 | 3.8048 | 6.3111 | 0.7037 | 0.8347 | 0.2996 | 0.3073 | 0.3091 | |
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| No log | 2.0 | 2 | 1.5559 | 3.2536 | 3.7731 | 6.2584 | 0.7017 | 0.8319 | 0.2998 | 0.3073 | 0.3092 | |
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| No log | 3.0 | 3 | 1.5513 | 3.2298 | 3.7401 | 6.2034 | 0.6997 | 0.8290 | 0.3002 | 0.3074 | 0.3092 | |
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| No log | 4.0 | 4 | 1.5469 | 3.2076 | 3.7083 | 6.1506 | 0.6977 | 0.8262 | 0.3006 | 0.3075 | 0.3093 | |
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| No log | 5.0 | 5 | 1.5434 | 3.1894 | 3.6815 | 6.1060 | 0.6961 | 0.8238 | 0.3011 | 0.3075 | 0.3093 | |
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| No log | 6.0 | 6 | 1.5407 | 3.1757 | 3.6614 | 6.0725 | 0.6949 | 0.8220 | 0.3015 | 0.3076 | 0.3094 | |
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| No log | 7.0 | 7 | 1.5387 | 3.1652 | 3.6460 | 6.0468 | 0.6940 | 0.8207 | 0.3017 | 0.3076 | 0.3094 | |
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| No log | 8.0 | 8 | 1.5371 | 3.1574 | 3.6348 | 6.0281 | 0.6933 | 0.8196 | 0.3019 | 0.3077 | 0.3094 | |
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| No log | 9.0 | 9 | 1.5361 | 3.1523 | 3.6273 | 6.0157 | 0.6928 | 0.8190 | 0.3020 | 0.3077 | 0.3094 | |
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| No log | 10.0 | 10 | 1.5356 | 3.1497 | 3.6237 | 6.0096 | 0.6926 | 0.8186 | 0.3020 | 0.3077 | 0.3094 | |
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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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