Model save

README.md

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+---
+license: apache-2.0
+base_model: microsoft/resnet-101
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: resnet-101-finetuned-CivilEng11k-newDS
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9932203389830508
+---
+
+<!-- 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. -->
+
+# resnet-101-finetuned-CivilEng11k-newDS
+
+This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4541
+- Accuracy: 0.9932
+
+## 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: 0.001
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 20
+- total_train_batch_size: 640
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 0.54  | 1    | 1.0986          | 0.4136   |
+| No log        | 1.62  | 3    | 1.0295          | 0.4339   |
+| No log        | 2.7   | 5    | 0.8537          | 0.4339   |
+| No log        | 3.78  | 7    | 0.6785          | 0.4441   |
+| No log        | 4.86  | 9    | 0.6141          | 0.6576   |
+| No log        | 5.95  | 11   | 0.5794          | 0.7559   |
+| No log        | 6.49  | 12   | 0.5616          | 0.8034   |
+| No log        | 7.57  | 14   | 0.5304          | 0.8475   |
+| No log        | 8.65  | 16   | 0.4964          | 0.9492   |
+| No log        | 9.73  | 18   | 0.4680          | 0.9864   |
+| 0.6919        | 10.81 | 20   | 0.4541          | 0.9932   |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 1.12.1
+- Datasets 2.18.0
+- Tokenizers 0.15.1