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-230530-193901
+  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-230530-193901
+
+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: 1.5356
+- Mae: 3.1497
+- Rmse: 3.6237
+- Abs Rel: 6.0096
+- Log Mae: 0.6926
+- Log Rmse: 0.8186
+- Delta1: 0.3020
+- Delta2: 0.3077
+- Delta3: 0.3094
+
+## 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    | 1.5604          | 3.2768 | 3.8048 | 6.3111  | 0.7037  | 0.8347   | 0.2996 | 0.3073 | 0.3091 |
+| No log        | 2.0   | 2    | 1.5559          | 3.2536 | 3.7731 | 6.2584  | 0.7017  | 0.8319   | 0.2998 | 0.3073 | 0.3092 |
+| No log        | 3.0   | 3    | 1.5513          | 3.2298 | 3.7401 | 6.2034  | 0.6997  | 0.8290   | 0.3002 | 0.3074 | 0.3092 |
+| No log        | 4.0   | 4    | 1.5469          | 3.2076 | 3.7083 | 6.1506  | 0.6977  | 0.8262   | 0.3006 | 0.3075 | 0.3093 |
+| No log        | 5.0   | 5    | 1.5434          | 3.1894 | 3.6815 | 6.1060  | 0.6961  | 0.8238   | 0.3011 | 0.3075 | 0.3093 |
+| No log        | 6.0   | 6    | 1.5407          | 3.1757 | 3.6614 | 6.0725  | 0.6949  | 0.8220   | 0.3015 | 0.3076 | 0.3094 |
+| No log        | 7.0   | 7    | 1.5387          | 3.1652 | 3.6460 | 6.0468  | 0.6940  | 0.8207   | 0.3017 | 0.3076 | 0.3094 |
+| No log        | 8.0   | 8    | 1.5371          | 3.1574 | 3.6348 | 6.0281  | 0.6933  | 0.8196   | 0.3019 | 0.3077 | 0.3094 |
+| No log        | 9.0   | 9    | 1.5361          | 3.1523 | 3.6273 | 6.0157  | 0.6928  | 0.8190   | 0.3020 | 0.3077 | 0.3094 |
+| No log        | 10.0  | 10   | 1.5356          | 3.1497 | 3.6237 | 6.0096  | 0.6926  | 0.8186   | 0.3020 | 0.3077 | 0.3094 |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Tokenizers 0.13.3