update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- vision
+- depth-estimation
+- generated_from_trainer
+model-index:
+- name: glpn-nyu-finetuned-diode-230530-204740
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# glpn-nyu-finetuned-diode-230530-204740
+
+This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5139
+- Mae: 3.0509
+- Rmse: 3.4756
+- Abs Rel: 5.7613
+- Log Mae: 0.6836
+- Log Rmse: 0.8048
+- Delta1: 0.3028
+- Delta2: 0.3079
+- Delta3: 0.3096
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 24
+- eval_batch_size: 48
+- seed: 2022
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Mae    | Rmse   | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
+| No log        | 1.0   | 1    | 1.5335          | 3.1427 | 3.6089 | 5.9847  | 0.6920  | 0.8173   | 0.3016 | 0.3077 | 0.3094 |
+| No log        | 2.0   | 2    | 1.5297          | 3.1246 | 3.5833 | 5.9419  | 0.6903  | 0.8149   | 0.3018 | 0.3077 | 0.3094 |
+| No log        | 3.0   | 3    | 1.5263          | 3.1085 | 3.5602 | 5.9033  | 0.6889  | 0.8128   | 0.3020 | 0.3078 | 0.3095 |
+| No log        | 4.0   | 4    | 1.5234          | 3.0947 | 3.5400 | 5.8694  | 0.6876  | 0.8109   | 0.3022 | 0.3078 | 0.3095 |
+| No log        | 5.0   | 5    | 1.5208          | 3.0825 | 3.5222 | 5.8395  | 0.6865  | 0.8092   | 0.3024 | 0.3079 | 0.3095 |
+| No log        | 6.0   | 6    | 1.5185          | 3.0723 | 3.5072 | 5.8144  | 0.6856  | 0.8078   | 0.3025 | 0.3079 | 0.3095 |
+| No log        | 7.0   | 7    | 1.5167          | 3.0639 | 3.4949 | 5.7937  | 0.6848  | 0.8067   | 0.3026 | 0.3079 | 0.3096 |
+| No log        | 8.0   | 8    | 1.5153          | 3.0574 | 3.4852 | 5.7775  | 0.6842  | 0.8057   | 0.3027 | 0.3079 | 0.3096 |
+| No log        | 9.0   | 9    | 1.5143          | 3.0531 | 3.4788 | 5.7667  | 0.6838  | 0.8051   | 0.3028 | 0.3079 | 0.3096 |
+| No log        | 10.0  | 10   | 1.5139          | 3.0509 | 3.4756 | 5.7613  | 0.6836  | 0.8048   | 0.3028 | 0.3079 | 0.3096 |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Tokenizers 0.13.3