diff --git a/examples/mlperf/model_eval.py b/examples/mlperf/model_eval.py
index 899484b4c5..60d76496a2 100644
--- a/examples/mlperf/model_eval.py
+++ b/examples/mlperf/model_eval.py
@@ -81,47 +81,42 @@ def eval_unet3d():
 
 def eval_retinanet():
   # RetinaNet with ResNeXt50_32X4D
+  from examples.mlperf.dataloader import batch_load_retinanet
+  from extra.datasets.openimages import normalize, download_dataset, BASEDIR
   from extra.models.resnet import ResNeXt50_32X4D
   from extra.models.retinanet import RetinaNet
-  mdl = RetinaNet(ResNeXt50_32X4D())
-  mdl.load_from_pretrained()
-
-  input_mean = Tensor([0.485, 0.456, 0.406]).reshape(1, -1, 1, 1)
-  input_std = Tensor([0.229, 0.224, 0.225]).reshape(1, -1, 1, 1)
-  def input_fixup(x):
-    x = x.permute([0,3,1,2]) / 255.0
-    x -= input_mean
-    x /= input_std
-    return x
-
-  from extra.datasets.openimages import download_dataset, iterate, BASEDIR
   from pycocotools.coco import COCO
   from pycocotools.cocoeval import COCOeval
   from contextlib import redirect_stdout
+  tlog("imports")
+
+  mdl = RetinaNet(ResNeXt50_32X4D())
+  mdl.load_from_pretrained()
+  tlog("loaded models")
+
   coco = COCO(download_dataset(base_dir:=getenv("BASE_DIR", BASEDIR), 'validation'))
   coco_eval = COCOeval(coco, iouType="bbox")
   coco_evalimgs, evaluated_imgs, ncats, narea = [], [], len(coco_eval.params.catIds), len(coco_eval.params.areaRng)
+  tlog("loaded dataset")
 
-  from tinygrad.engine.jit import TinyJit
-  mdlrun = TinyJit(lambda x: mdl(input_fixup(x)).realize())
-
-  n, bs = 0, 8
+  iterator = batch_load_retinanet(coco, True, Path(base_dir), getenv("BS", 8), shuffle=False)
+  def data_get():
+    x, img_ids, img_sizes, cookie = next(iterator)
+    return x.to(Device.DEFAULT).realize(), img_ids, img_sizes, cookie
+  n = 0
+  proc = data_get()
+  tlog("loaded initial data")
   st = time.perf_counter()
-  for x, targets in iterate(coco, base_dir, bs):
-    dat = Tensor(x.astype(np.float32))
-    mt = time.perf_counter()
-    if dat.shape[0] == bs:
-      outs = mdlrun(dat).numpy()
-    else:
-      mdlrun._jit_cache = []
-      outs =  mdl(input_fixup(dat)).numpy()
-    et = time.perf_counter()
-    predictions = mdl.postprocess_detections(outs, input_size=dat.shape[1:3], orig_image_sizes=[t["image_size"] for t in targets])
-    ext = time.perf_counter()
-    n += len(targets)
-    print(f"[{n}/{len(coco.imgs)}] == {(mt-st)*1000:.2f} ms loading data, {(et-mt)*1000:.2f} ms to run model, {(ext-et)*1000:.2f} ms for postprocessing")
-    img_ids = [t["image_id"] for t in targets]
-    coco_results  = [{"image_id": targets[i]["image_id"], "category_id": label, "bbox": box.tolist(), "score": score}
+  while proc is not None:
+    GlobalCounters.reset()
+    proc = (mdl(normalize(proc[0])), proc[1], proc[2], proc[3])
+    run = time.perf_counter()
+    # load the next data here
+    try: next_proc = data_get()
+    except StopIteration: next_proc = None
+    nd = time.perf_counter()
+    predictions, img_ids = mdl.postprocess_detections(proc[0].numpy(), orig_image_sizes=proc[2]), proc[1]
+    coco_results  = [{"image_id": img_ids[i], "category_id": label, "bbox": box.tolist(), "score": score}
       for i, prediction in enumerate(predictions) for box, score, label in zip(*prediction.values())]
     with redirect_stdout(None):
       coco_eval.cocoDt = coco.loadRes(coco_results)
@@ -129,13 +124,18 @@ def eval_retinanet():
       coco_eval.evaluate()
     evaluated_imgs.extend(img_ids)
     coco_evalimgs.append(np.array(coco_eval.evalImgs).reshape(ncats, narea, len(img_ids)))
-    st = time.perf_counter()
+    n += len(proc[0])
+    et = time.perf_counter()
+    tlog(f"****** {(run-st)*1000:7.2f} ms to enqueue, {(et-run)*1000:7.2f} ms to realize ({(nd-run)*1000:7.2f} ms fetching). {(len(proc))/(et-st):8.2f} examples/sec. {GlobalCounters.global_ops*1e-12/(et-st):5.2f} TFLOPS")
+    st = et
+    proc, next_proc = next_proc, None
 
   coco_eval.params.imgIds = evaluated_imgs
   coco_eval._paramsEval.imgIds = evaluated_imgs
   coco_eval.evalImgs = list(np.concatenate(coco_evalimgs, -1).flatten())
   coco_eval.accumulate()
   coco_eval.summarize()
+  tlog("done")
 
 def eval_rnnt():
   # RNN-T
diff --git a/extra/datasets/openimages.py b/extra/datasets/openimages.py
index d54cadb3f5..ee461d7685 100644
--- a/extra/datasets/openimages.py
+++ b/extra/datasets/openimages.py
@@ -205,6 +205,12 @@ def resize(img:Image, tgt:dict[str, np.ndarray|tuple]|None=None, size:tuple[int,
 
   return img, img_size
 
+def normalize(img:Tensor, device:list[str]|None = None):
+  mean = Tensor([0.485, 0.456, 0.406], device=device, dtype=dtypes.float32).reshape(1, -1, 1, 1)
+  std = Tensor([0.229, 0.224, 0.225], device=device, dtype=dtypes.float32).reshape(1, -1, 1, 1)
+  img = ((img.permute([0, 3, 1, 2]) / 255.0) - mean) / std
+  return img.cast(dtypes.default_float)
+
 if __name__ == "__main__":
   download_dataset(base_dir:=getenv("BASE_DIR", BASEDIR), "train")
   download_dataset(base_dir, "validation")