Add TF image classification example script (#19956)

* TF image classification script

* Update requirements

* Fix up

* Add tests

* Update test fetcher
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Fix directory path

* Adding `zero-shot-object-detection` pipeline doctest. (#20274)

* Adding `zero-shot-object-detection` pipeline doctest.

* Remove nested_simplify.

* Add generate kwargs to `AutomaticSpeechRecognitionPipeline` (#20952)

* Add generate kwargs to AutomaticSpeechRecognitionPipeline

* Add test for generation kwargs

* Trigger CI

* Data collator returns np

* Update feature extractor -> image processor

* Bug fixes - updates to reflect changes in API

* Update flags to match PT & run faster

* Update instructions - Maria's comment

* Update examples/tensorflow/image-classification/README.md

* Remove slow decorator

---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: bofeng huang <bofenghuang7@gmail.com>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>

examples/tensorflow/test_tensorflow_examples.py

@@ -38,6 +38,7 @@ SRC_DIRS = [
         "question-answering",
         "summarization",
         "translation",
+        "image-classification",
     ]
 ]
 sys.path.extend(SRC_DIRS)
@@ -45,6 +46,7 @@ sys.path.extend(SRC_DIRS)
 
 if SRC_DIRS is not None:
     import run_clm
+    import run_image_classification
     import run_mlm
     import run_ner
     import run_qa as run_squad
@@ -294,3 +296,28 @@ class ExamplesTests(TestCasePlus):
             run_translation.main()
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["bleu"], 30)
+
+    def test_run_image_classification(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_image_classification.py
+            --dataset_name hf-internal-testing/cats_vs_dogs_sample
+            --model_name_or_path microsoft/resnet-18
+            --do_train
+            --do_eval
+            --learning_rate 1e-4
+            --per_device_train_batch_size 2
+            --per_device_eval_batch_size 1
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            --dataloader_num_workers 16
+            --num_train_epochs 2
+            --train_val_split 0.1
+            --seed 42
+            --ignore_mismatched_sizes True
+            """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_image_classification.main()
+            result = get_results(tmp_dir)
+            self.assertGreaterEqual(result["accuracy"], 0.7)