Update README.md

README.md

@@ -1,249 +1,249 @@
----
-license: apache-2.0
-task_categories:
-- object-detection
-tags:
-- COCO
-- Detection
-- '2017'
-pretty_name: COCO detection dataset script
-size_categories:
-- 100K<n<1M
-dataset_info:
-  config_name: '2017'
-  features:
-  - name: id
-    dtype: int64
-  - name: objects
-    struct:
-    - name: bbox_id
-      sequence: int64
-    - name: category_id
-      sequence:
-        class_label:
-          names:
-            '0': N/A
-            '1': person
-            '2': bicycle
-            '3': car
-            '4': motorcycle
-            '5': airplane
-            '6': bus
-            '7': train
-            '8': truck
-            '9': boat
-            '10': traffic light
-            '11': fire hydrant
-            '12': street sign
-            '13': stop sign
-            '14': parking meter
-            '15': bench
-            '16': bird
-            '17': cat
-            '18': dog
-            '19': horse
-            '20': sheep
-            '21': cow
-            '22': elephant
-            '23': bear
-            '24': zebra
-            '25': giraffe
-            '26': hat
-            '27': backpack
-            '28': umbrella
-            '29': shoe
-            '30': eye glasses
-            '31': handbag
-            '32': tie
-            '33': suitcase
-            '34': frisbee
-            '35': skis
-            '36': snowboard
-            '37': sports ball
-            '38': kite
-            '39': baseball bat
-            '40': baseball glove
-            '41': skateboard
-            '42': surfboard
-            '43': tennis racket
-            '44': bottle
-            '45': plate
-            '46': wine glass
-            '47': cup
-            '48': fork
-            '49': knife
-            '50': spoon
-            '51': bowl
-            '52': banana
-            '53': apple
-            '54': sandwich
-            '55': orange
-            '56': broccoli
-            '57': carrot
-            '58': hot dog
-            '59': pizza
-            '60': donut
-            '61': cake
-            '62': chair
-            '63': couch
-            '64': potted plant
-            '65': bed
-            '66': mirror
-            '67': dining table
-            '68': window
-            '69': desk
-            '70': toilet
-            '71': door
-            '72': tv
-            '73': laptop
-            '74': mouse
-            '75': remote
-            '76': keyboard
-            '77': cell phone
-            '78': microwave
-            '79': oven
-            '80': toaster
-            '81': sink
-            '82': refrigerator
-            '83': blender
-            '84': book
-            '85': clock
-            '86': vase
-            '87': scissors
-            '88': teddy bear
-            '89': hair drier
-            '90': toothbrush
-    - name: bbox
-      sequence:
-        sequence: float64
-        length: 4
-    - name: iscrowd
-      sequence: int64
-    - name: area
-      sequence: float64
-  - name: height
-    dtype: int64
-  - name: width
-    dtype: int64
-  - name: file_name
-    dtype: string
-  - name: coco_url
-    dtype: string
-  - name: image_path
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 87231216
-    num_examples: 117266
-  - name: validation
-    num_bytes: 3692192
-    num_examples: 4952
-  download_size: 20405354669
-  dataset_size: 90923408
----
-## Usage
-For using the COCO dataset (2017), you need to download it manually first:
-```bash
-wget http://images.cocodataset.org/zips/train2017.zip
-wget http://images.cocodataset.org/zips/val2017.zip
-wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
-```
-
-Then to load the dataset:
-```python
-COCO_DIR = ...(path to the downloaded dataset directory)...
-ds = datasets.load_dataset(
-    "yonigozlan/coco_2017_detection_script",
-    "2017",
-    data_dir=COCO_DIR,
-    trust_remote_code=True,
-)
-```
-
-## Benchmarking
-Here is an example of how to benchmark a 🤗 Transformers object detection model on the validation data of the COCO dataset:
-
-```python
-import datasets
-import torch
-from PIL import Image
-from torch.utils.data import DataLoader
-from torchmetrics.detection.mean_ap import MeanAveragePrecision
-from tqdm import tqdm
-
-from transformers import AutoImageProcessor, AutoModelForObjectDetection
-
-# prepare data
-COCO_DIR = ...(path to the downloaded dataset directory)...
-ds = datasets.load_dataset(
-    "yonigozlan/coco_2017_detection_script",
-    "2017",
-    data_dir=COCO_DIR,
-    trust_remote_code=True,
-)
-val_data = ds["validation"]
-categories = val_data.features["objects"]["category_id"].feature.names
-id2label = {index: x for index, x in enumerate(categories, start=0)}
-label2id = {v: k for k, v in id2label.items()}
-checkpoint = "facebook/detr-resnet-50"
-
-# load model and processor
-model = AutoModelForObjectDetection.from_pretrained(
-    checkpoint, torch_dtype=torch.float16
-).to("cuda")
-id2label_model = model.config.id2label
-processor = AutoImageProcessor.from_pretrained(checkpoint)
-
-
-def collate_fn(batch):
-    data = {}
-    images = [Image.open(x["image_path"]).convert("RGB") for x in batch]
-    data["images"] = images
-    annotations = []
-    for x in batch:
-        boxes = x["objects"]["bbox"]
-        # convert to xyxy format
-        boxes = [[box[0], box[1], box[0] + box[2], box[1] + box[3]] for box in boxes]
-        labels = x["objects"]["category_id"]
-        boxes = torch.tensor(boxes)
-        labels = torch.tensor(labels)
-        annotations.append({"boxes": boxes, "labels": labels})
-    data["original_size"] = [(x["height"], x["width"]) for x in batch]
-    data["annotations"] = annotations
-    return data
-
-
-# prepare dataloader
-dataloader = DataLoader(val_data, batch_size=8, collate_fn=collate_fn)
-
-# prepare metric
-metric = MeanAveragePrecision(box_format="xyxy", class_metrics=True)
-
-# evaluation loop
-for i, batch in tqdm(enumerate(dataloader), total=len(dataloader)):
-    inputs = (
-        processor(batch["images"], return_tensors="pt").to("cuda").to(torch.float16)
-    )
-    with torch.no_grad():
-        outputs = model(**inputs)
-    target_sizes = torch.tensor([x for x in batch["original_size"]]).to("cuda")
-    results = processor.post_process_object_detection(
-        outputs, threshold=0.0, target_sizes=target_sizes
-    )
-
-    # convert predicted label id to dataset label id
-    if len(id2label_model) != len(id2label):
-        for result in results:
-            result["labels"] = torch.tensor(
-                [label2id.get(id2label_model[x.item()], 0) for x in result["labels"]]
-            )
-    # put results back to cpu
-    for result in results:
-        for k, v in result.items():
-            if isinstance(v, torch.Tensor):
-                result[k] = v.to("cpu")
-    metric.update(results, batch["annotations"])
-
-metrics = metric.compute()
-print(metrics)
-```
+---
+license: cc-by-4.0
+task_categories:
+- object-detection
+tags:
+- COCO
+- Detection
+- '2017'
+pretty_name: COCO detection dataset script
+size_categories:
+- 100K<n<1M
+dataset_info:
+  config_name: '2017'
+  features:
+  - name: id
+    dtype: int64
+  - name: objects
+    struct:
+    - name: bbox_id
+      sequence: int64
+    - name: category_id
+      sequence:
+        class_label:
+          names:
+            '0': N/A
+            '1': person
+            '2': bicycle
+            '3': car
+            '4': motorcycle
+            '5': airplane
+            '6': bus
+            '7': train
+            '8': truck
+            '9': boat
+            '10': traffic light
+            '11': fire hydrant
+            '12': street sign
+            '13': stop sign
+            '14': parking meter
+            '15': bench
+            '16': bird
+            '17': cat
+            '18': dog
+            '19': horse
+            '20': sheep
+            '21': cow
+            '22': elephant
+            '23': bear
+            '24': zebra
+            '25': giraffe
+            '26': hat
+            '27': backpack
+            '28': umbrella
+            '29': shoe
+            '30': eye glasses
+            '31': handbag
+            '32': tie
+            '33': suitcase
+            '34': frisbee
+            '35': skis
+            '36': snowboard
+            '37': sports ball
+            '38': kite
+            '39': baseball bat
+            '40': baseball glove
+            '41': skateboard
+            '42': surfboard
+            '43': tennis racket
+            '44': bottle
+            '45': plate
+            '46': wine glass
+            '47': cup
+            '48': fork
+            '49': knife
+            '50': spoon
+            '51': bowl
+            '52': banana
+            '53': apple
+            '54': sandwich
+            '55': orange
+            '56': broccoli
+            '57': carrot
+            '58': hot dog
+            '59': pizza
+            '60': donut
+            '61': cake
+            '62': chair
+            '63': couch
+            '64': potted plant
+            '65': bed
+            '66': mirror
+            '67': dining table
+            '68': window
+            '69': desk
+            '70': toilet
+            '71': door
+            '72': tv
+            '73': laptop
+            '74': mouse
+            '75': remote
+            '76': keyboard
+            '77': cell phone
+            '78': microwave
+            '79': oven
+            '80': toaster
+            '81': sink
+            '82': refrigerator
+            '83': blender
+            '84': book
+            '85': clock
+            '86': vase
+            '87': scissors
+            '88': teddy bear
+            '89': hair drier
+            '90': toothbrush
+    - name: bbox
+      sequence:
+        sequence: float64
+        length: 4
+    - name: iscrowd
+      sequence: int64
+    - name: area
+      sequence: float64
+  - name: height
+    dtype: int64
+  - name: width
+    dtype: int64
+  - name: file_name
+    dtype: string
+  - name: coco_url
+    dtype: string
+  - name: image_path
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 87231216
+    num_examples: 117266
+  - name: validation
+    num_bytes: 3692192
+    num_examples: 4952
+  download_size: 20405354669
+  dataset_size: 90923408
+---
+## Usage
+For using the COCO dataset (2017), you need to download it manually first:
+```bash
+wget http://images.cocodataset.org/zips/train2017.zip
+wget http://images.cocodataset.org/zips/val2017.zip
+wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+```
+
+Then to load the dataset:
+```python
+COCO_DIR = ...(path to the downloaded dataset directory)...
+ds = datasets.load_dataset(
+    "yonigozlan/coco_2017_detection_script",
+    "2017",
+    data_dir=COCO_DIR,
+    trust_remote_code=True,
+)
+```
+
+## Benchmarking
+Here is an example of how to benchmark a 🤗 Transformers object detection model on the validation data of the COCO dataset:
+
+```python
+import datasets
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader
+from torchmetrics.detection.mean_ap import MeanAveragePrecision
+from tqdm import tqdm
+
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+
+# prepare data
+COCO_DIR = ...(path to the downloaded dataset directory)...
+ds = datasets.load_dataset(
+    "yonigozlan/coco_2017_detection_script",
+    "2017",
+    data_dir=COCO_DIR,
+    trust_remote_code=True,
+)
+val_data = ds["validation"]
+categories = val_data.features["objects"]["category_id"].feature.names
+id2label = {index: x for index, x in enumerate(categories, start=0)}
+label2id = {v: k for k, v in id2label.items()}
+checkpoint = "facebook/detr-resnet-50"
+
+# load model and processor
+model = AutoModelForObjectDetection.from_pretrained(
+    checkpoint, torch_dtype=torch.float16
+).to("cuda")
+id2label_model = model.config.id2label
+processor = AutoImageProcessor.from_pretrained(checkpoint)
+
+
+def collate_fn(batch):
+    data = {}
+    images = [Image.open(x["image_path"]).convert("RGB") for x in batch]
+    data["images"] = images
+    annotations = []
+    for x in batch:
+        boxes = x["objects"]["bbox"]
+        # convert to xyxy format
+        boxes = [[box[0], box[1], box[0] + box[2], box[1] + box[3]] for box in boxes]
+        labels = x["objects"]["category_id"]
+        boxes = torch.tensor(boxes)
+        labels = torch.tensor(labels)
+        annotations.append({"boxes": boxes, "labels": labels})
+    data["original_size"] = [(x["height"], x["width"]) for x in batch]
+    data["annotations"] = annotations
+    return data
+
+
+# prepare dataloader
+dataloader = DataLoader(val_data, batch_size=8, collate_fn=collate_fn)
+
+# prepare metric
+metric = MeanAveragePrecision(box_format="xyxy", class_metrics=True)
+
+# evaluation loop
+for i, batch in tqdm(enumerate(dataloader), total=len(dataloader)):
+    inputs = (
+        processor(batch["images"], return_tensors="pt").to("cuda").to(torch.float16)
+    )
+    with torch.no_grad():
+        outputs = model(**inputs)
+    target_sizes = torch.tensor([x for x in batch["original_size"]]).to("cuda")
+    results = processor.post_process_object_detection(
+        outputs, threshold=0.0, target_sizes=target_sizes
+    )
+
+    # convert predicted label id to dataset label id
+    if len(id2label_model) != len(id2label):
+        for result in results:
+            result["labels"] = torch.tensor(
+                [label2id.get(id2label_model[x.item()], 0) for x in result["labels"]]
+            )
+    # put results back to cpu
+    for result in results:
+        for k, v in result.items():
+            if isinstance(v, torch.Tensor):
+                result[k] = v.to("cpu")
+    metric.update(results, batch["annotations"])
+
+metrics = metric.compute()
+print(metrics)
+```