Saving best model to hub

README.md

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+---
+license: apache-2.0
+base_model: microsoft/resnet-101
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: resnet101_rvl-cdip
+  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. -->
+
+# resnet101_rvl-cdip
+
+This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6158
+- Accuracy: 0.8210
+- Brier Loss: 0.2556
+- Nll: 1.7696
+- F1 Micro: 0.8210
+- F1 Macro: 0.8209
+- Ece: 0.0176
+- Aurc: 0.0418
+
+## 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: 2e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- 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
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Brier Loss | Nll    | F1 Micro | F1 Macro | Ece    | Aurc   |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
+| 1.3521        | 1.0   | 5000  | 1.2626          | 0.6133   | 0.5108     | 2.7262 | 0.6133   | 0.6042   | 0.0455 | 0.1644 |
+| 0.942         | 2.0   | 10000 | 0.9005          | 0.7318   | 0.3723     | 2.2139 | 0.7318   | 0.7293   | 0.0174 | 0.0862 |
+| 0.7983        | 3.0   | 15000 | 0.7691          | 0.7723   | 0.3198     | 2.0444 | 0.7723   | 0.7714   | 0.0139 | 0.0641 |
+| 0.7167        | 4.0   | 20000 | 0.7048          | 0.7924   | 0.2931     | 1.9414 | 0.7924   | 0.7931   | 0.0135 | 0.0541 |
+| 0.6656        | 5.0   | 25000 | 0.6658          | 0.8052   | 0.2770     | 1.8581 | 0.8052   | 0.8056   | 0.0108 | 0.0486 |
+| 0.6252        | 6.0   | 30000 | 0.6415          | 0.8117   | 0.2670     | 1.8157 | 0.8117   | 0.8112   | 0.0128 | 0.0455 |
+| 0.6038        | 7.0   | 35000 | 0.6269          | 0.8176   | 0.2607     | 1.7833 | 0.8176   | 0.8180   | 0.0144 | 0.0432 |
+| 0.5784        | 8.0   | 40000 | 0.6217          | 0.8195   | 0.2583     | 1.7723 | 0.8195   | 0.8195   | 0.0151 | 0.0425 |
+| 0.5583        | 9.0   | 45000 | 0.6150          | 0.8214   | 0.2553     | 1.7719 | 0.8214   | 0.8214   | 0.0164 | 0.0415 |
+| 0.5519        | 10.0  | 50000 | 0.6158          | 0.8210   | 0.2556     | 1.7696 | 0.8210   | 0.8209   | 0.0176 | 0.0418 |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.2.0.dev20231002
+- Datasets 2.7.1
+- Tokenizers 0.13.3

all_results.json

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+{
+    "epoch": 10.0,
+    "train_loss": 0.8447013873291016,
+    "train_runtime": 30071.7313,
+    "train_samples_per_second": 106.412,
+    "train_steps_per_second": 1.663
+}

config.json

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+{
+  "_name_or_path": "microsoft/resnet-101",
+  "architectures": [
+    "ResNetForImageClassification"
+  ],
+  "depths": [
+    3,
+    4,
+    23,
+    3
+  ],
+  "downsample_in_first_stage": false,
+  "embedding_size": 64,
+  "hidden_act": "relu",
+  "hidden_sizes": [
+    256,
+    512,
+    1024,
+    2048
+  ],
+  "id2label": {
+    "0": "letter",
+    "1": "form",
+    "2": "email",
+    "3": "handwritten",
+    "4": "advertisement",
+    "5": "scientific_report",
+    "6": "scientific_publication",
+    "7": "specification",
+    "8": "file_folder",
+    "9": "news_article",
+    "10": "budget",
+    "11": "invoice",
+    "12": "presentation",
+    "13": "questionnaire",
+    "14": "resume",
+    "15": "memo"
+  },
+  "label2id": {
+    "advertisement": 4,
+    "budget": 10,
+    "email": 2,
+    "file_folder": 8,
+    "form": 1,
+    "handwritten": 3,
+    "invoice": 11,
+    "letter": 0,
+    "memo": 15,
+    "news_article": 9,
+    "presentation": 12,
+    "questionnaire": 13,
+    "resume": 14,
+    "scientific_publication": 6,
+    "scientific_report": 5,
+    "specification": 7
+  },
+  "layer_type": "bottleneck",
+  "model_type": "resnet",
+  "num_channels": 3,
+  "out_features": [
+    "stage4"
+  ],
+  "out_indices": [
+    4
+  ],
+  "problem_type": "single_label_classification",
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.3"
+}

pytorch_model.bin

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train_results.json

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+{
+    "epoch": 10.0,
+    "train_loss": 0.8447013873291016,
+    "train_runtime": 30071.7313,
+    "train_samples_per_second": 106.412,
+    "train_steps_per_second": 1.663
+}

training_args.bin

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