Librarian Bot: Add base_model information to model

This pull request aims to enrich the metadata of your model by adding [`microsoft/resnet-50`](https://huggingface.co/microsoft/resnet-50) as a `base_model` field, situated in the `YAML` block of your model's `README.md`.

How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection.

**Why add this?** Enhancing your model's metadata in this way:
- **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- **Highlights Impact** - It showcases the contributions and influences different models have within the community.

For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer).

This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, queries, or need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien). Your input is invaluable to us!

README.md

@@ -1,30 +1,31 @@
 ---
+language:
+- en
 license: apache-2.0
 tags:
-  - vision
-  - image-classification
-  - generated_from_trainer
+- vision
+- image-classification
+- generated_from_trainer
 datasets:
-  - imagefolder
-model-index:
-  - name: fruits-and-vegetables-detector-36
-    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: 0.9721
-language:
-  - en
+- imagefolder
 pipeline_tag: image-classification
+base_model: microsoft/resnet-50
+model-index:
+- name: fruits-and-vegetables-detector-36
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - type: accuracy
+      value: 0.9721
+      name: Accuracy
 ---
 
 # fruits-and-vegetables-detector-36