update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: resnet_50_base_aihub_model_py
+  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: 0.9680951259712739
+    - name: Precision
+      type: precision
+      value: 0.9712201145310659
+    - name: Recall
+      type: recall
+      value: 0.9624241107598097
+    - name: F1
+      type: f1
+      value: 0.9667111625354762
+---
+
+<!-- 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_50_base_aihub_model_py
+
+This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0987
+- Accuracy: 0.9681
+- Precision: 0.9712
+- Recall: 0.9624
+- F1: 0.9667
+
+## 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: 5e-05
+- train_batch_size: 128
+- eval_batch_size: 128
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.5577        | 1.0   | 149  | 0.4027          | 0.8453   | 0.8514    | 0.8415 | 0.8435 |
+| 0.323         | 2.0   | 299  | 0.2346          | 0.9097   | 0.9208    | 0.8962 | 0.9074 |
+| 0.2467        | 3.0   | 448  | 0.1786          | 0.9303   | 0.9465    | 0.9216 | 0.9326 |
+| 0.1953        | 4.0   | 598  | 0.1266          | 0.9573   | 0.9591    | 0.9483 | 0.9535 |
+| 0.1456        | 4.98  | 745  | 0.0987          | 0.9681   | 0.9712    | 0.9624 | 0.9667 |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.12.0
+- Tokenizers 0.13.3