Change no trainer image_classification test (#17635)

* Adjust test arguments and use a new example test

examples/pytorch/test_accelerate_examples.py

@@ -300,19 +300,25 @@ class ExamplesTestsNoTrainer(TestCasePlus):
         tmp_dir = self.get_auto_remove_tmp_dir()
         testargs = f"""
             {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
-            --dataset_name huggingface/image-classification-test-sample
-            --output_dir {tmp_dir}
-            --num_warmup_steps=8
-            --learning_rate=3e-3
-            --per_device_train_batch_size=2
-            --per_device_eval_batch_size=1
-            --checkpointing_steps epoch
-            --with_tracking
+            --model_name_or_path google/vit-base-patch16-224-in21k
+            --dataset_name hf-internal-testing/cats_vs_dogs_sample
+            --learning_rate 1e-4
+            --per_device_train_batch_size 2
+            --per_device_eval_batch_size 1
+            --max_train_steps 2
+            --train_val_split 0.1
             --seed 42
+            --output_dir {tmp_dir}
+            --with_tracking
+            --checkpointing_steps 1
         """.split()
 
+        if is_cuda_and_apex_available():
+            testargs.append("--fp16")
+
         _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
         result = get_results(tmp_dir)
-        self.assertGreaterEqual(result["eval_accuracy"], 0.50)
-        self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
+        # The base model scores a 25%
+        self.assertGreaterEqual(result["eval_accuracy"], 0.625)
+        self.assertTrue(os.path.exists(os.path.join(tmp_dir, "step_1")))
         self.assertTrue(os.path.exists(os.path.join(tmp_dir, "image_classification_no_trainer")))