feat: update object detection labels in config.json and corresponding usage example in README.

README.md

@@ -1,199 +1,91 @@
 ---
+license: apache-2.0
+tags:
+- object-detection
+- vision
+datasets:
+- coco
+pipeline_tag: object-detection
 library_name: transformers
-tags: []
 ---
 
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+# LW-DETR (Light-Weight Detection Transformer)
+
+LW-DETR, a Light-Weight DEtection TRansformer model, is designed to be a real-time object detection alternative that outperforms conventional convolutional (YOLO-style) and earlier transformer-based (DETR) methods in terms of speed and accuracy trade-off. It was introduced in the paper [LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection](https://huggingface.co/papers/2406.03459) by Chen et al. and first released in this repository.
+Disclaimer: This model was originally contributed by [stevenbucaille](https://huggingface.co/stevenbucaille) in 🤗 transformers.
+
+## Model description
+
+LW-DETR is an end-to-end object detection model that uses a Vision Transformer (ViT) backbone as its encoder, a simple convolutional projector, and a shallow DETR decoder. The core philosophy is to leverage the power of transformers while implementing several efficiency-focused techniques to achieve real-time performance.
+
+Key Architectural Details:
+- ViT Encoder: Uses a plain ViT architecture. To reduce the quadratic complexity of global self-attention, it adopts interleaved window and global attentions.
+- Window-Major Organization: It employs a highly efficient window-major feature map organization scheme for attention computation, which drastically reduces the costly memory permutation operations required when transitioning between global and window attention modes, leading to lower inference latency.
+- Feature Aggregation: It aggregates features from multiple levels (intermediate and final layers) of the ViT encoder to create richer input for the decoder.
+- Projector: A C2f block (from YOLOv8) connects the encoder and decoder. For larger versions (large/xlarge), it outputs two-scale features ($1/8$ and $1/32$) to the decoder.
+- Shallow DETR Decoder: It uses a computationally efficient 3-layer transformer decoder (instead of the standard 6 layers), incorporating deformable cross-attention for faster convergence and lower latency.
+- Object Queries: It uses a mixed-query selection scheme to form the object queries from both learnable content queries and generated spatial queries (based on top-K features from the Projector).
+
+Training Details:
+- IoU-aware Classification Loss (IA-BCE loss): Enhances the classification branch by incorporating IoU information into the target score $t=s^{\alpha}u^{1-\alpha}$.
+- Group DETR: Uses a Group DETR strategy (13 parallel weight-sharing decoders) for faster training convergence without affecting inference speed.
+- Pretraining: Uses a two-stage pretraining strategy: first, ViT is pretrained on Objects365 using a Masked Image Modeling (MIM) method (CAEv2), followed by supervised retraining of the encoder and training of the projector and decoder on Objects365. This provides a significant performance boost (average of $\approx 5.5\text{ mAP}$).
+  
+### How to use
+
+You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=stevenbucaille/lw-detr) to look for all available LW DETR models.
+
+Here is how to use this model:
+
+```python
+from transformers import AutoImageProcessor, LwDetrForObjectDetection
+import torch
+from PIL import Image
+import requests
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+processor = AutoImageProcessor.from_pretrained("stevenbucaille/lwdetr_tiny_30e_objects365")
+model = LwDetrForObjectDetection.from_pretrained("stevenbucaille/lwdetr_tiny_30e_objects365")
+
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+
+# convert outputs (bounding boxes and class logits) to COCO API
+# let's only keep detections with score > 0.7
+target_sizes = torch.tensor([image.size[::-1]])
+results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]
+
+for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+    box = [round(i, 2) for i in box.tolist()]
+    print(
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+    )
+```
+This should output:
+```
+Detected Jug with confidence 0.868 at location [4.89, 56.55, 319.81, 474.79]
+Detected Refrigerator with confidence 0.753 at location [40.56, 73.11, 176.3, 116.86]
+Detected Jug with confidence 0.718 at location [340.33, 25.1, 640.32, 368.68]
+```
+
+Currently, both the feature extractor and model support PyTorch. 
+
+## Training data
+
+The LW-DETR models are trained/finetuned on the following datasets:
+- Pretraining: Primarily conducted on [Objects365](https://www.objects365.org/overview.html), a large-scale, high-quality dataset for object detection.
+- Finetuning: Final training is performed on the standard [COCO 2017 object detection dataset](https://cocodataset.org/#home).
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{chen2024lw,
+        title={LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection},
+        author={Chen, Qiang and Su, Xiangbo and Zhang, Xinyu and Wang, Jian and Chen, Jiahui and Shen, Yunpeng and Han, Chuchu and Chen, Ziliang and Xu, Weixiang and Li, Fanrong and others},
+        journal={arXiv preprint arXiv:2406.03459},
+        year={2024}
+    }
+```

config.json

@@ -75,741 +75,741 @@
   "group_detr": 13,
   "hidden_expansion": 0.5,
   "id2label": {
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-    "1": "LABEL_1",
-    "2": "LABEL_2",
-    "3": "LABEL_3",
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-    "363": "LABEL_363",
-    "364": "LABEL_364",
-    "365": "LABEL_365"
+    "0": "Person",
+    "1": "Sneakers",
+    "10": "Cup",
+    "100": "Hanger",
+    "101": "Blackboard/Whiteboard",
+    "102": "Napkin",
+    "103": "Other Fish",
+    "104": "Orange/Tangerine",
+    "105": "Toiletry",
+    "106": "Keyboard",
+    "107": "Tomato",
+    "108": "Lantern",
+    "109": "Machinery Vehicle",
+    "11": "Street Lights",
+    "110": "Fan",
+    "111": "Green Vegetables",
+    "112": "Banana",
+    "113": "Baseball Glove",
+    "114": "Airplane",
+    "115": "Mouse",
+    "116": "Train",
+    "117": "Pumpkin",
+    "118": "Soccer",
+    "119": "Skiboard",
+    "12": "Cabinet/shelf",
+    "120": "Luggage",
+    "121": "Nightstand",
+    "122": "Tea pot",
+    "123": "Telephone",
+    "124": "Trolley",
+    "125": "Head Phone",
+    "126": "Sports Car",
+    "127": "Stop Sign",
+    "128": "Dessert",
+    "129": "Scooter",
+    "13": "Handbag/Satchel",
+    "130": "Stroller",
+    "131": "Crane",
+    "132": "Remote",
+    "133": "Refrigerator",
+    "134": "Oven",
+    "135": "Lemon",
+    "136": "Duck",
+    "137": "Baseball Bat",
+    "138": "Surveillance Camera",
+    "139": "Cat",
+    "14": "Bracelet",
+    "140": "Jug",
+    "141": "Broccoli",
+    "142": "Piano",
+    "143": "Pizza",
+    "144": "Elephant",
+    "145": "Skateboard",
+    "146": "Surfboard",
+    "147": "Gun",
+    "148": "Skating and Skiing shoes",
+    "149": "Gas stove",
+    "15": "Plate",
+    "150": "Donut",
+    "151": "Bow Tie",
+    "152": "Carrot",
+    "153": "Toilet",
+    "154": "Kite",
+    "155": "Strawberry",
+    "156": "Other Balls",
+    "157": "Shovel",
+    "158": "Pepper",
+    "159": "Computer Box",
+    "16": "Picture/Frame",
+    "160": "Toilet Paper",
+    "161": "Cleaning Products",
+    "162": "Chopsticks",
+    "163": "Microwave",
+    "164": "Pigeon",
+    "165": "Baseball",
+    "166": "Cutting/chopping Board",
+    "167": "Coffee Table",
+    "168": "Side Table",
+    "169": "Scissors",
+    "17": "Helmet",
+    "170": "Marker",
+    "171": "Pie",
+    "172": "Ladder",
+    "173": "Snowboard",
+    "174": "Cookies",
+    "175": "Radiator",
+    "176": "Fire Hydrant",
+    "177": "Basketball",
+    "178": "Zebra",
+    "179": "Grape",
+    "18": "Book",
+    "180": "Giraffe",
+    "181": "Potato",
+    "182": "Sausage",
+    "183": "Tricycle",
+    "184": "Violin",
+    "185": "Egg",
+    "186": "Fire Extinguisher",
+    "187": "Candy",
+    "188": "Fire Truck",
+    "189": "Billiards",
+    "19": "Gloves",
+    "190": "Converter",
+    "191": "Bathtub",
+    "192": "Wheelchair",
+    "193": "Golf Club",
+    "194": "Briefcase",
+    "195": "Cucumber",
+    "196": "Cigar/Cigarette",
+    "197": "Paint Brush",
+    "198": "Pear",
+    "199": "Heavy Truck",
+    "2": "Chair",
+    "20": "Storage box",
+    "200": "Hamburger",
+    "201": "Extractor",
+    "202": "Extension Cord",
+    "203": "Tong",
+    "204": "Tennis Racket",
+    "205": "Folder",
+    "206": "American Football",
+    "207": "earphone",
+    "208": "Mask",
+    "209": "Kettle",
+    "21": "Boat",
+    "210": "Tennis",
+    "211": "Ship",
+    "212": "Swing",
+    "213": "Coffee Machine",
+    "214": "Slide",
+    "215": "Carriage",
+    "216": "Onion",
+    "217": "Green beans",
+    "218": "Projector",
+    "219": "Frisbee",
+    "22": "Leather Shoes",
+    "220": "Washing Machine/Drying Machine",
+    "221": "Chicken",
+    "222": "Printer",
+    "223": "Watermelon",
+    "224": "Saxophone",
+    "225": "Tissue",
+    "226": "Toothbrush",
+    "227": "Ice cream",
+    "228": "Hot-air balloon",
+    "229": "Cello",
+    "23": "Flower",
+    "230": "French Fries",
+    "231": "Scale",
+    "232": "Trophy",
+    "233": "Cabbage",
+    "234": "Hot dog",
+    "235": "Blender",
+    "236": "Peach",
+    "237": "Rice",
+    "238": "Wallet/Purse",
+    "239": "Volleyball",
+    "24": "Bench",
+    "240": "Deer",
+    "241": "Goose",
+    "242": "Tape",
+    "243": "Tablet",
+    "244": "Cosmetics",
+    "245": "Trumpet",
+    "246": "Pineapple",
+    "247": "Golf Ball",
+    "248": "Ambulance",
+    "249": "Parking meter",
+    "25": "Potted Plant",
+    "250": "Mango",
+    "251": "Key",
+    "252": "Hurdle",
+    "253": "Fishing Rod",
+    "254": "Medal",
+    "255": "Flute",
+    "256": "Brush",
+    "257": "Penguin",
+    "258": "Megaphone",
+    "259": "Corn",
+    "26": "Bowl/Basin",
+    "260": "Lettuce",
+    "261": "Garlic",
+    "262": "Swan",
+    "263": "Helicopter",
+    "264": "Green Onion",
+    "265": "Sandwich",
+    "266": "Nuts",
+    "267": "Speed Limit Sign",
+    "268": "Induction Cooker",
+    "269": "Broom",
+    "27": "Flag",
+    "270": "Trombone",
+    "271": "Plum",
+    "272": "Rickshaw",
+    "273": "Goldfish",
+    "274": "Kiwi fruit",
+    "275": "Router/modem",
+    "276": "Poker Card",
+    "277": "Toaster",
+    "278": "Shrimp",
+    "279": "Sushi",
+    "28": "Pillow",
+    "280": "Cheese",
+    "281": "Notepaper",
+    "282": "Cherry",
+    "283": "Pliers",
+    "284": "CD",
+    "285": "Pasta",
+    "286": "Hammer",
+    "287": "Cue",
+    "288": "Avocado",
+    "289": "Hami melon",
+    "29": "Boots",
+    "290": "Flask",
+    "291": "Mushroom",
+    "292": "Screwdriver",
+    "293": "Soap",
+    "294": "Recorder",
+    "295": "Bear",
+    "296": "Eggplant",
+    "297": "Board Eraser",
+    "298": "Coconut",
+    "299": "Tape Measure/Ruler",
+    "3": "Other Shoes",
+    "30": "Vase",
+    "300": "Pig",
+    "301": "Showerhead",
+    "302": "Globe",
+    "303": "Chips",
+    "304": "Steak",
+    "305": "Crosswalk Sign",
+    "306": "Stapler",
+    "307": "Camel",
+    "308": "Formula 1",
+    "309": "Pomegranate",
+    "31": "Microphone",
+    "310": "Dishwasher",
+    "311": "Crab",
+    "312": "Hoverboard",
+    "313": "Meatball",
+    "314": "Rice Cooker",
+    "315": "Tuba",
+    "316": "Calculator",
+    "317": "Papaya",
+    "318": "Antelope",
+    "319": "Parrot",
+    "32": "Necklace",
+    "320": "Seal",
+    "321": "Butterfly",
+    "322": "Dumbbell",
+    "323": "Donkey",
+    "324": "Lion",
+    "325": "Urinal",
+    "326": "Dolphin",
+    "327": "Electric Drill",
+    "328": "Hair Dryer",
+    "329": "Egg tart",
+    "33": "Ring",
+    "330": "Jellyfish",
+    "331": "Treadmill",
+    "332": "Lighter",
+    "333": "Grapefruit",
+    "334": "Game board",
+    "335": "Mop",
+    "336": "Radish",
+    "337": "Baozi",
+    "338": "Target",
+    "339": "French",
+    "34": "SUV",
+    "340": "Spring Rolls",
+    "341": "Monkey",
+    "342": "Rabbit",
+    "343": "Pencil Case",
+    "344": "Yak",
+    "345": "Red Cabbage",
+    "346": "Binoculars",
+    "347": "Asparagus",
+    "348": "Barbell",
+    "349": "Scallop",
+    "35": "Wine Glass",
+    "350": "Noddles",
+    "351": "Comb",
+    "352": "Dumpling",
+    "353": "Oyster",
+    "354": "Table Tennis paddle",
+    "355": "Cosmetics Brush/Eyeliner Pencil",
+    "356": "Chainsaw",
+    "357": "Eraser",
+    "358": "Lobster",
+    "359": "Durian",
+    "36": "Belt",
+    "360": "Okra",
+    "361": "Lipstick",
+    "362": "Cosmetics Mirror",
+    "363": "Curling",
+    "364": "Table Tennis",
+    "365": "N/A",
+    "37": "Monitor/TV",
+    "38": "Backpack",
+    "39": "Umbrella",
+    "4": "Hat",
+    "40": "Traffic Light",
+    "41": "Speaker",
+    "42": "Watch",
+    "43": "Tie",
+    "44": "Trash bin Can",
+    "45": "Slippers",
+    "46": "Bicycle",
+    "47": "Stool",
+    "48": "Barrel/bucket",
+    "49": "Van",
+    "5": "Car",
+    "50": "Couch",
+    "51": "Sandals",
+    "52": "Basket",
+    "53": "Drum",
+    "54": "Pen/Pencil",
+    "55": "Bus",
+    "56": "Wild Bird",
+    "57": "High Heels",
+    "58": "Motorcycle",
+    "59": "Guitar",
+    "6": "Lamp",
+    "60": "Carpet",
+    "61": "Cell Phone",
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+    "63": "Camera",
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+    "65": "Truck",
+    "66": "Traffic cone",
+    "67": "Cymbal",
+    "68": "Lifesaver",
+    "69": "Towel",
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+    "70": "Stuffed Toy",
+    "71": "Candle",
+    "72": "Sailboat",
+    "73": "Laptop",
+    "74": "Awning",
+    "75": "Bed",
+    "76": "Faucet",
+    "77": "Tent",
+    "78": "Horse",
+    "79": "Mirror",
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+    "80": "Power outlet",
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+    "82": "Apple",
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+    "84": "Knife",
+    "85": "Hockey Stick",
+    "86": "Paddle",
+    "87": "Pickup Truck",
+    "88": "Fork",
+    "89": "Traffic Sign",
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+    "90": "Balloon",
+    "91": "Tripod",
+    "92": "Dog",
+    "93": "Spoon",
+    "94": "Clock",
+    "95": "Pot",
+    "96": "Cow",
+    "97": "Cake",
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   "model_type": "lw_detr",
   "num_feature_levels": 1,
@@ -824,4 +824,4 @@
   "transformers_version": "5.0.0.dev0",
   "use_pretrained_backbone": false,
   "use_timm_backbone": false
-}
+}