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NotSarahConnor1984
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detr_finetuned_coco

Object Detection
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conditional_detr
Generated from Trainer
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  1. README.md +232 -0
  2. config.json +219 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +45 -0
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/conditional-detr-resnet-50
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: detr_finetuned_coco
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # detr_finetuned_coco
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+
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+ This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.1466
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+ - Map: 0.0055
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+ - Map 50: 0.011
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+ - Map 75: 0.005
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+ - Map Small: 0.0262
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+ - Map Medium: 0.0039
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+ - Map Large: 0.0059
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+ - Mar 1: 0.0175
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+ - Mar 10: 0.0272
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+ - Mar 100: 0.0319
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+ - Mar Small: 0.0821
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+ - Mar Medium: 0.0199
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+ - Mar Large: 0.0329
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+ - Map Person: 0.1641
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+ - Mar 100 Person: 0.6212
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+ - Map Bicycle: 0.0
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+ - Mar 100 Bicycle: 0.0
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+ - Map Car: 0.0149
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+ - Mar 100 Car: 0.1743
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+ - Map Motorcycle: 0.0
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+ - Mar 100 Motorcycle: 0.0
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+ - Map Airplane: 0.0022
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+ - Mar 100 Airplane: 0.036
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+ - Map Bus: 0.0
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+ - Mar 100 Bus: 0.0
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+ - Map Train: 0.0
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+ - Mar 100 Train: 0.0
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+ - Map Truck: 0.0
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+ - Mar 100 Truck: 0.0
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+ - Map Boat: 0.0
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+ - Mar 100 Boat: 0.0
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+ - Map Traffic light: 0.0
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+ - Mar 100 Traffic light: 0.0
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+ - Map Fire hydrant: 0.0
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+ - Mar 100 Fire hydrant: 0.0
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+ - Map Stop sign: 0.0
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+ - Mar 100 Stop sign: 0.0
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+ - Map Parking meter: 0.0
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+ - Mar 100 Parking meter: 0.0
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+ - Map Bench: 0.0002
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+ - Mar 100 Bench: 0.0034
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+ - Map Bird: 0.0002
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+ - Mar 100 Bird: 0.0172
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+ - Map Cat: 0.0215
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+ - Mar 100 Cat: 0.1765
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+ - Map Dog: 0.0
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+ - Mar 100 Dog: 0.0
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+ - Map Horse: 0.0
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+ - Mar 100 Horse: 0.0
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+ - Map Sheep: 0.0
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+ - Mar 100 Sheep: 0.0
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+ - Map Cow: 0.0
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+ - Mar 100 Cow: 0.0
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+ - Map Elephant: 0.0045
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+ - Mar 100 Elephant: 0.06
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+ - Map Bear: 0.0
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+ - Mar 100 Bear: 0.0
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+ - Map Zebra: 0.0
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+ - Mar 100 Zebra: 0.0
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+ - Map Giraffe: 0.0012
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+ - Mar 100 Giraffe: 0.12
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+ - Map Backpack: 0.0
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+ - Mar 100 Backpack: 0.0
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+ - Map Umbrella: 0.0002
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+ - Mar 100 Umbrella: 0.0263
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+ - Map Handbag: 0.0
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+ - Mar 100 Handbag: 0.0
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+ - Map Tie: 0.0
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+ - Mar 100 Tie: 0.0
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+ - Map Suitcase: 0.0023
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+ - Mar 100 Suitcase: 0.0233
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+ - Map Frisbee: 0.0
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+ - Mar 100 Frisbee: 0.0
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+ - Map Skis: 0.0
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+ - Mar 100 Skis: 0.0
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+ - Map Snowboard: 0.0
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+ - Mar 100 Snowboard: 0.0
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+ - Map Sports ball: 0.0
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+ - Mar 100 Sports ball: 0.0
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+ - Map Kite: 0.0
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+ - Mar 100 Kite: 0.0
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+ - Map Baseball bat: 0.0
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+ - Mar 100 Baseball bat: 0.0
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+ - Map Baseball glove: 0.0
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+ - Mar 100 Baseball glove: 0.0
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+ - Map Skateboard: 0.0
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+ - Mar 100 Skateboard: 0.0
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+ - Map Surfboard: 0.0
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+ - Mar 100 Surfboard: 0.0
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+ - Map Tennis racket: 0.0
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+ - Mar 100 Tennis racket: 0.0
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+ - Map Bottle: 0.0073
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+ - Mar 100 Bottle: 0.1237
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+ - Map Wine glass: 0.0
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+ - Mar 100 Wine glass: 0.0
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+ - Map Cup: 0.0062
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+ - Mar 100 Cup: 0.0875
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+ - Map Fork: 0.0
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+ - Mar 100 Fork: 0.0
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+ - Map Knife: 0.0
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+ - Mar 100 Knife: 0.0
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+ - Map Spoon: 0.0
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+ - Mar 100 Spoon: 0.0
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+ - Map Bowl: 0.0
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+ - Mar 100 Bowl: 0.0
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+ - Map Banana: 0.0
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+ - Mar 100 Banana: 0.0
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+ - Map Apple: 0.0008
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+ - Mar 100 Apple: 0.04
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+ - Map Sandwich: 0.0
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+ - Mar 100 Sandwich: 0.0
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+ - Map Orange: 0.0
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+ - Mar 100 Orange: 0.0
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+ - Map Broccoli: 0.0005
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+ - Mar 100 Broccoli: 0.0261
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+ - Map Carrot: 0.0
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+ - Mar 100 Carrot: 0.0
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+ - Map Hot dog: 0.0
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+ - Mar 100 Hot dog: 0.0
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+ - Map Pizza: 0.1168
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+ - Mar 100 Pizza: 0.37
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+ - Map Donut: 0.0
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+ - Mar 100 Donut: 0.0
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+ - Map Cake: 0.0
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+ - Mar 100 Cake: 0.0
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+ - Map Chair: 0.0082
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+ - Mar 100 Chair: 0.1747
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+ - Map Couch: 0.0
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+ - Mar 100 Couch: 0.0
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+ - Map Potted plant: 0.0
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+ - Mar 100 Potted plant: 0.0
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+ - Map Bed: 0.0069
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+ - Mar 100 Bed: 0.1571
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+ - Map Dining table: 0.063
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+ - Mar 100 Dining table: 0.21
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+ - Map Toilet: 0.0
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+ - Mar 100 Toilet: 0.0
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+ - Map Tv: 0.0
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+ - Mar 100 Tv: 0.0
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+ - Map Laptop: 0.0
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+ - Mar 100 Laptop: 0.0
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+ - Map Mouse: 0.0
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+ - Mar 100 Mouse: 0.0
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+ - Map Remote: 0.0
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+ - Mar 100 Remote: 0.0
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+ - Map Keyboard: 0.0
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+ - Mar 100 Keyboard: 0.0
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+ - Map Cell phone: 0.0
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+ - Mar 100 Cell phone: 0.0
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+ - Map Microwave: 0.0
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+ - Mar 100 Microwave: 0.0
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+ - Map Oven: 0.0
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+ - Mar 100 Oven: 0.0
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+ - Map Sink: 0.0
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+ - Mar 100 Sink: 0.0
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+ - Map Refrigerator: 0.0
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+ - Mar 100 Refrigerator: 0.0
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+ - Map Book: 0.0001
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+ - Mar 100 Book: 0.0059
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+ - Map Clock: 0.0
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+ - Mar 100 Clock: 0.0
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+ - Map Vase: 0.0
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+ - Mar 100 Vase: 0.0
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+ - Map Scissors: 0.0
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+ - Mar 100 Scissors: 0.0
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+ - Map Teddy bear: 0.0
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+ - Mar 100 Teddy bear: 0.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Person | Mar 100 Person | Map Bicycle | Mar 100 Bicycle | Map Car | Mar 100 Car | Map Motorcycle | Mar 100 Motorcycle | Map Airplane | Mar 100 Airplane | Map Bus | Mar 100 Bus | Map Train | Mar 100 Train | Map Truck | Mar 100 Truck | Map Boat | Mar 100 Boat | Map Traffic light | Mar 100 Traffic light | Map Fire hydrant | Mar 100 Fire hydrant | Map Stop sign | Mar 100 Stop sign | Map Parking meter | Mar 100 Parking meter | Map Bench | Mar 100 Bench | Map Bird | Mar 100 Bird | Map Cat | Mar 100 Cat | Map Dog | Mar 100 Dog | Map Horse | Mar 100 Horse | Map Sheep | Mar 100 Sheep | Map Cow | Mar 100 Cow | Map Elephant | Mar 100 Elephant | Map Bear | Mar 100 Bear | Map Zebra | Mar 100 Zebra | Map Giraffe | Mar 100 Giraffe | Map Backpack | Mar 100 Backpack | Map Umbrella | Mar 100 Umbrella | Map Handbag | Mar 100 Handbag | Map Tie | Mar 100 Tie | Map Suitcase | Mar 100 Suitcase | Map Frisbee | Mar 100 Frisbee | Map Skis | Mar 100 Skis | Map Snowboard | Mar 100 Snowboard | Map Sports ball | Mar 100 Sports ball | Map Kite | Mar 100 Kite | Map Baseball bat | Mar 100 Baseball bat | Map Baseball glove | Mar 100 Baseball glove | Map Skateboard | Mar 100 Skateboard | Map Surfboard | Mar 100 Surfboard | Map Tennis racket | Mar 100 Tennis racket | Map Bottle | Mar 100 Bottle | Map Wine glass | Mar 100 Wine glass | Map Cup | Mar 100 Cup | Map Fork | Mar 100 Fork | Map Knife | Mar 100 Knife | Map Spoon | Mar 100 Spoon | Map Bowl | Mar 100 Bowl | Map Banana | Mar 100 Banana | Map Apple | Mar 100 Apple | Map Sandwich | Mar 100 Sandwich | Map Orange | Mar 100 Orange | Map Broccoli | Mar 100 Broccoli | Map Carrot | Mar 100 Carrot | Map Hot dog | Mar 100 Hot dog | Map Pizza | Mar 100 Pizza | Map Donut | Mar 100 Donut | Map Cake | Mar 100 Cake | Map Chair | Mar 100 Chair | Map Couch | Mar 100 Couch | Map Potted plant | Mar 100 Potted plant | Map Bed | Mar 100 Bed | Map Dining table | Mar 100 Dining table | Map Toilet | Mar 100 Toilet | Map Tv | Mar 100 Tv | Map Laptop | Mar 100 Laptop | Map Mouse | Mar 100 Mouse | Map Remote | Mar 100 Remote | Map Keyboard | Mar 100 Keyboard | Map Cell phone | Mar 100 Cell phone | Map Microwave | Mar 100 Microwave | Map Oven | Mar 100 Oven | Map Sink | Mar 100 Sink | Map Refrigerator | Mar 100 Refrigerator | Map Book | Mar 100 Book | Map Clock | Mar 100 Clock | Map Vase | Mar 100 Vase | Map Scissors | Mar 100 Scissors | Map Teddy bear | Mar 100 Teddy bear |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:----------:|:--------------:|:-----------:|:---------------:|:-------:|:-----------:|:--------------:|:------------------:|:------------:|:----------------:|:-------:|:-----------:|:---------:|:-------------:|:---------:|:-------------:|:--------:|:------------:|:-----------------:|:---------------------:|:----------------:|:--------------------:|:-------------:|:-----------------:|:-----------------:|:---------------------:|:---------:|:-------------:|:--------:|:------------:|:-------:|:-----------:|:-------:|:-----------:|:---------:|:-------------:|:---------:|:-------------:|:-------:|:-----------:|:------------:|:----------------:|:--------:|:------------:|:---------:|:-------------:|:-----------:|:---------------:|:------------:|:----------------:|:------------:|:----------------:|:-----------:|:---------------:|:-------:|:-----------:|:------------:|:----------------:|:-----------:|:---------------:|:--------:|:------------:|:-------------:|:-----------------:|:---------------:|:-------------------:|:--------:|:------------:|:----------------:|:--------------------:|:------------------:|:----------------------:|:--------------:|:------------------:|:-------------:|:-----------------:|:-----------------:|:---------------------:|:----------:|:--------------:|:--------------:|:------------------:|:-------:|:-----------:|:--------:|:------------:|:---------:|:-------------:|:---------:|:-------------:|:--------:|:------------:|:----------:|:--------------:|:---------:|:-------------:|:------------:|:----------------:|:----------:|:--------------:|:------------:|:----------------:|:----------:|:--------------:|:-----------:|:---------------:|:---------:|:-------------:|:---------:|:-------------:|:--------:|:------------:|:---------:|:-------------:|:---------:|:-------------:|:----------------:|:--------------------:|:-------:|:-----------:|:----------------:|:--------------------:|:----------:|:--------------:|:------:|:----------:|:----------:|:--------------:|:---------:|:-------------:|:----------:|:--------------:|:------------:|:----------------:|:--------------:|:------------------:|:-------------:|:-----------------:|:--------:|:------------:|:--------:|:------------:|:----------------:|:--------------------:|:--------:|:------------:|:---------:|:-------------:|:--------:|:------------:|:------------:|:----------------:|:--------------:|:------------------:|
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+ | No log | 1.0 | 125 | 2.6574 | 0.0003 | 0.0007 | 0.0002 | 0.0 | 0.0003 | 0.0004 | 0.0002 | 0.0013 | 0.0059 | 0.0 | 0.0046 | 0.0066 | 0.0194 | 0.458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 2.0 | 250 | 2.4915 | 0.0006 | 0.0015 | 0.0004 | 0.0 | 0.0007 | 0.0008 | 0.0004 | 0.0022 | 0.0069 | 0.0 | 0.0056 | 0.0077 | 0.0477 | 0.535 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 3.0 | 375 | 2.4253 | 0.0007 | 0.0017 | 0.0005 | 0.0 | 0.0009 | 0.0009 | 0.0006 | 0.0029 | 0.0071 | 0.0 | 0.0062 | 0.0077 | 0.0552 | 0.5355 | 0.0 | 0.0 | 0.0001 | 0.0046 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
218
+ | 2.6522 | 4.0 | 500 | 2.3606 | 0.0011 | 0.0029 | 0.0008 | 0.0007 | 0.0013 | 0.0014 | 0.002 | 0.0042 | 0.0084 | 0.0071 | 0.0079 | 0.0089 | 0.0723 | 0.5438 | 0.0 | 0.0 | 0.0002 | 0.0099 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0008 | 0.0169 | 0.0 | 0.0 | 0.0021 | 0.0089 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0111 | 0.05 | 0.0013 | 0.018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
219
+ | 2.6522 | 5.0 | 625 | 2.2637 | 0.0025 | 0.0049 | 0.0021 | 0.0167 | 0.0021 | 0.0029 | 0.0105 | 0.0144 | 0.0186 | 0.05 | 0.0113 | 0.0196 | 0.1111 | 0.5908 | 0.0 | 0.0 | 0.008 | 0.1086 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0032 | 0.0294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.004 | 0.0533 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0061 | 0.0322 | 0.0 | 0.0 | 0.0014 | 0.0321 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0314 | 0.24 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0088 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0121 | 0.1643 | 0.0157 | 0.166 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0059 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
220
+ | 2.6522 | 6.0 | 750 | 2.2451 | 0.0036 | 0.0065 | 0.0029 | 0.0 | 0.0024 | 0.0039 | 0.0112 | 0.0161 | 0.0203 | 0.0 | 0.0124 | 0.0214 | 0.1236 | 0.5871 | 0.0 | 0.0 | 0.0057 | 0.1204 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.003 | 0.0294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0037 | 0.06 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.02 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0011 | 0.0627 | 0.0 | 0.0 | 0.0095 | 0.0482 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0014 | 0.0261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0833 | 0.25 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0017 | 0.0516 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0087 | 0.1429 | 0.0348 | 0.164 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0008 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
221
+ | 2.6522 | 7.0 | 875 | 2.1905 | 0.0048 | 0.0101 | 0.0043 | 0.025 | 0.0038 | 0.0052 | 0.0173 | 0.0261 | 0.0304 | 0.075 | 0.017 | 0.0317 | 0.1612 | 0.6068 | 0.0 | 0.0 | 0.0141 | 0.1711 | 0.0 | 0.0 | 0.0018 | 0.032 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0174 | 0.1706 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0031 | 0.06 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0023 | 0.1533 | 0.0 | 0.0 | 0.0002 | 0.0263 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0083 | 0.1085 | 0.0 | 0.0 | 0.0038 | 0.0714 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0009 | 0.04 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0004 | 0.0261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0888 | 0.37 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0041 | 0.1253 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0089 | 0.1643 | 0.0558 | 0.21 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0067 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
222
+ | 2.2266 | 8.0 | 1000 | 2.1653 | 0.0051 | 0.0102 | 0.0049 | 0.0262 | 0.0038 | 0.0054 | 0.0166 | 0.0258 | 0.0302 | 0.0786 | 0.0192 | 0.0312 | 0.1617 | 0.6175 | 0.0 | 0.0 | 0.0134 | 0.175 | 0.0 | 0.0 | 0.0029 | 0.036 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0003 | 0.0034 | 0.0 | 0.0 | 0.0114 | 0.1176 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0048 | 0.06 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0014 | 0.12 | 0.0 | 0.0 | 0.0002 | 0.0263 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.004 | 0.1068 | 0.0 | 0.0 | 0.0036 | 0.0929 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1104 | 0.37 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0064 | 0.1571 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0137 | 0.2071 | 0.0558 | 0.206 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0059 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
223
+ | 2.2266 | 9.0 | 1125 | 2.1483 | 0.0059 | 0.0117 | 0.0058 | 0.0265 | 0.0039 | 0.0063 | 0.0182 | 0.0277 | 0.0325 | 0.0821 | 0.02 | 0.0336 | 0.1617 | 0.6231 | 0.0 | 0.0 | 0.0132 | 0.1697 | 0.0 | 0.0 | 0.0024 | 0.036 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0034 | 0.0002 | 0.0172 | 0.0206 | 0.1765 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0037 | 0.06 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0011 | 0.12 | 0.0 | 0.0 | 0.0002 | 0.0263 | 0.0 | 0.0 | 0.0 | 0.0 | 0.002 | 0.0233 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0072 | 0.122 | 0.0 | 0.0 | 0.0062 | 0.0893 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0008 | 0.04 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1481 | 0.37 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0086 | 0.1868 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0124 | 0.2 | 0.0626 | 0.21 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0059 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
224
+ | 2.2266 | 10.0 | 1250 | 2.1466 | 0.0055 | 0.011 | 0.005 | 0.0262 | 0.0039 | 0.0059 | 0.0175 | 0.0272 | 0.0319 | 0.0821 | 0.0199 | 0.0329 | 0.1641 | 0.6212 | 0.0 | 0.0 | 0.0149 | 0.1743 | 0.0 | 0.0 | 0.0022 | 0.036 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0034 | 0.0002 | 0.0172 | 0.0215 | 0.1765 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0045 | 0.06 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0012 | 0.12 | 0.0 | 0.0 | 0.0002 | 0.0263 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0023 | 0.0233 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0073 | 0.1237 | 0.0 | 0.0 | 0.0062 | 0.0875 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0008 | 0.04 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1168 | 0.37 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0082 | 0.1747 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0069 | 0.1571 | 0.063 | 0.21 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0059 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
225
+
226
+
227
+ ### Framework versions
228
+
229
+ - Transformers 4.41.1
230
+ - Pytorch 2.2.2+cu121
231
+ - Datasets 2.19.1
232
+ - Tokenizers 0.19.1
config.json ADDED
@@ -0,0 +1,219 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "microsoft/conditional-detr-resnet-50",
3
+ "activation_dropout": 0.0,
4
+ "activation_function": "relu",
5
+ "architectures": [
6
+ "ConditionalDetrForObjectDetection"
7
+ ],
8
+ "attention_dropout": 0.0,
9
+ "auxiliary_loss": false,
10
+ "backbone": "resnet50",
11
+ "backbone_config": null,
12
+ "backbone_kwargs": {
13
+ "in_chans": 3,
14
+ "out_indices": [
15
+ 1,
16
+ 2,
17
+ 3,
18
+ 4
19
+ ]
20
+ },
21
+ "bbox_cost": 5,
22
+ "bbox_loss_coefficient": 5,
23
+ "class_cost": 2,
24
+ "cls_loss_coefficient": 2,
25
+ "d_model": 256,
26
+ "decoder_attention_heads": 8,
27
+ "decoder_ffn_dim": 2048,
28
+ "decoder_layerdrop": 0.0,
29
+ "decoder_layers": 6,
30
+ "dice_loss_coefficient": 1,
31
+ "dilation": false,
32
+ "dropout": 0.1,
33
+ "encoder_attention_heads": 8,
34
+ "encoder_ffn_dim": 2048,
35
+ "encoder_layerdrop": 0.0,
36
+ "encoder_layers": 6,
37
+ "focal_alpha": 0.25,
38
+ "giou_cost": 2,
39
+ "giou_loss_coefficient": 2,
40
+ "id2label": {
41
+ "0": "person",
42
+ "1": "bicycle",
43
+ "2": "car",
44
+ "3": "motorcycle",
45
+ "4": "airplane",
46
+ "5": "bus",
47
+ "6": "train",
48
+ "7": "truck",
49
+ "8": "boat",
50
+ "9": "traffic light",
51
+ "10": "fire hydrant",
52
+ "11": "stop sign",
53
+ "12": "parking meter",
54
+ "13": "bench",
55
+ "14": "bird",
56
+ "15": "cat",
57
+ "16": "dog",
58
+ "17": "horse",
59
+ "18": "sheep",
60
+ "19": "cow",
61
+ "20": "elephant",
62
+ "21": "bear",
63
+ "22": "zebra",
64
+ "23": "giraffe",
65
+ "24": "backpack",
66
+ "25": "umbrella",
67
+ "26": "handbag",
68
+ "27": "tie",
69
+ "28": "suitcase",
70
+ "29": "frisbee",
71
+ "30": "skis",
72
+ "31": "snowboard",
73
+ "32": "sports ball",
74
+ "33": "kite",
75
+ "34": "baseball bat",
76
+ "35": "baseball glove",
77
+ "36": "skateboard",
78
+ "37": "surfboard",
79
+ "38": "tennis racket",
80
+ "39": "bottle",
81
+ "40": "wine glass",
82
+ "41": "cup",
83
+ "42": "fork",
84
+ "43": "knife",
85
+ "44": "spoon",
86
+ "45": "bowl",
87
+ "46": "banana",
88
+ "47": "apple",
89
+ "48": "sandwich",
90
+ "49": "orange",
91
+ "50": "broccoli",
92
+ "51": "carrot",
93
+ "52": "hot dog",
94
+ "53": "pizza",
95
+ "54": "donut",
96
+ "55": "cake",
97
+ "56": "chair",
98
+ "57": "couch",
99
+ "58": "potted plant",
100
+ "59": "bed",
101
+ "60": "dining table",
102
+ "61": "toilet",
103
+ "62": "tv",
104
+ "63": "laptop",
105
+ "64": "mouse",
106
+ "65": "remote",
107
+ "66": "keyboard",
108
+ "67": "cell phone",
109
+ "68": "microwave",
110
+ "69": "oven",
111
+ "70": "toaster",
112
+ "71": "sink",
113
+ "72": "refrigerator",
114
+ "73": "book",
115
+ "74": "clock",
116
+ "75": "vase",
117
+ "76": "scissors",
118
+ "77": "teddy bear",
119
+ "78": "hair drier",
120
+ "79": "toothbrush"
121
+ },
122
+ "init_std": 0.02,
123
+ "init_xavier_std": 1.0,
124
+ "is_encoder_decoder": true,
125
+ "label2id": {
126
+ "airplane": 4,
127
+ "apple": 47,
128
+ "backpack": 24,
129
+ "banana": 46,
130
+ "baseball bat": 34,
131
+ "baseball glove": 35,
132
+ "bear": 21,
133
+ "bed": 59,
134
+ "bench": 13,
135
+ "bicycle": 1,
136
+ "bird": 14,
137
+ "boat": 8,
138
+ "book": 73,
139
+ "bottle": 39,
140
+ "bowl": 45,
141
+ "broccoli": 50,
142
+ "bus": 5,
143
+ "cake": 55,
144
+ "car": 2,
145
+ "carrot": 51,
146
+ "cat": 15,
147
+ "cell phone": 67,
148
+ "chair": 56,
149
+ "clock": 74,
150
+ "couch": 57,
151
+ "cow": 19,
152
+ "cup": 41,
153
+ "dining table": 60,
154
+ "dog": 16,
155
+ "donut": 54,
156
+ "elephant": 20,
157
+ "fire hydrant": 10,
158
+ "fork": 42,
159
+ "frisbee": 29,
160
+ "giraffe": 23,
161
+ "hair drier": 78,
162
+ "handbag": 26,
163
+ "horse": 17,
164
+ "hot dog": 52,
165
+ "keyboard": 66,
166
+ "kite": 33,
167
+ "knife": 43,
168
+ "laptop": 63,
169
+ "microwave": 68,
170
+ "motorcycle": 3,
171
+ "mouse": 64,
172
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