update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- fashion_mnist_quality_drift
+metrics:
+- accuracy
+- f1
+model-index:
+- name: resnet-50-fashion-mnist-quality-drift
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: fashion_mnist_quality_drift
+      type: fashion_mnist_quality_drift
+      config: default
+      split: training
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.73
+    - name: F1
+      type: f1
+      value: 0.7289360255705818
+---
+
+<!-- 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-fashion-mnist-quality-drift
+
+This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fashion_mnist_quality_drift dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7473
+- Accuracy: 0.73
+- F1: 0.7289
+
+## 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: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 1.5138        | 1.0   | 750  | 0.9237          | 0.684    | 0.6826 |
+| 0.9377        | 2.0   | 1500 | 0.7861          | 0.722    | 0.7253 |
+| 0.8276        | 3.0   | 2250 | 0.7473          | 0.73     | 0.7289 |
+
+
+### Framework versions
+
+- Transformers 4.21.0
+- Pytorch 1.12.0+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1