dfine_coco2017-1k

benjamintli/dfine_coco2017-1k

dfine_coco2017-1k is a object detection model from benjamintli. Use it when you need objects located in an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Object Detection
Library: transformers
License: apache-2.0
Model type: d_fine
Access: Public
Created: Jan 13, 2026
Updated: Jan 14, 2026
SHA: a58111d7

Base models

ustc-community/dfine-large-obj365

Model card

From the Hugging Face model README

dfine_coco2017-1k

This model is a fine-tuned version of ustc-community/dfine-large-obj365 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.0118
Map: 0.0299
Map 50: 0.0372
Map 75: 0.0329
Map Airplane: 0.0221
Map Apple: 0.0
Map Backpack: 0.0021
Map Banana: 0.0063
Map Baseball bat: 0.0
Map Baseball glove: 0.1984
Map Bear: 0.0
Map Bed: 0.0
Map Bench: 0.0009
Map Bicycle: 0.0
Map Bird: 0.0016
Map Boat: 0.0123
Map Book: 0.0002
Map Bottle: 0.046
Map Bowl: 0.0588
Map Broccoli: 0.0007
Map Bus: 0.3589
Map Cake: 0.0142
Map Car: 0.1625
Map Carrot: 0.0
Map Cat: 0.0047
Map Cell phone: 0.08
Map Chair: 0.0741
Map Clock: 0.001
Map Couch: 0.0125
Map Cow: 0.1326
Map Cup: 0.0592
Map Dining table: 0.0596
Map Dog: 0.0
Map Donut: -1.0
Map Elephant: 0.0034
Map Fire hydrant: -1.0
Map Fork: 0.0027
Map Frisbee: 0.0008
Map Giraffe: 0.0976
Map Handbag: 0.0022
Map Horse: 0.0022
Map Hot dog: 0.0
Map Keyboard: 0.0
Map Kite: 0.008
Map Knife: 0.001
Map Laptop: 0.0009
Map Large: 0.0435
Map Medium: 0.0369
Map Microwave: 0.0
Map Motorcycle: 0.0006
Map Mouse: 0.0
Map Orange: 0.0
Map Oven: 0.0
Map Parking meter: 0.0
Map Person: 0.1633
Map Pizza: 0.0451
Map Potted plant: 0.0026
Map Refrigerator: 0.0013
Map Remote: 0.0053
Map Sandwich: 0.0
Map Scissors: 0.0
Map Sheep: 0.0
Map Sink: 0.0001
Map Skateboard: 0.0
Map Skis: 0.0049
Map Small: 0.0085
Map Snowboard: 0.0
Map Spoon: 0.0
Map Sports ball: 0.0
Map Stop sign: 0.006
Map Suitcase: 0.0009
Map Surfboard: 0.0027
Map Teddy bear: 0.0
Map Tennis racket: 0.1978
Map Tie: 0.0
Map Toaster: -1.0
Map Toilet: 0.0101
Map Toothbrush: 0.0
Map Traffic light: 0.0388
Map Train: 0.0096
Map Truck: 0.108
Map Tv: 0.2457
Map Umbrella: 0.0001
Map Vase: 0.0002
Map Wine glass: 0.0
Map Zebra: 0.0015
Mar 1: 0.1037
Mar 10: 0.1704
Mar 100: 0.1887
Mar 100 Airplane: 0.6857
Mar 100 Apple: 0.0
Mar 100 Backpack: 0.0333
Mar 100 Banana: 0.0545
Mar 100 Baseball bat: 0.0
Mar 100 Baseball glove: 0.3438
Mar 100 Bear: 0.0
Mar 100 Bed: 0.0
Mar 100 Bench: 0.275
Mar 100 Bicycle: 0.0
Mar 100 Bird: 0.5
Mar 100 Boat: 0.2408
Mar 100 Book: 0.0344
Mar 100 Bottle: 0.3444
Mar 100 Bowl: 0.224
Mar 100 Broccoli: 0.1143
Mar 100 Bus: 0.7917
Mar 100 Cake: 0.25
Mar 100 Car: 0.3451
Mar 100 Carrot: 0.0
Mar 100 Cat: 0.3692
Mar 100 Cell phone: 0.1474
Mar 100 Chair: 0.2458
Mar 100 Clock: 0.1412
Mar 100 Couch: 0.3455
Mar 100 Cow: 0.4625
Mar 100 Cup: 0.2686
Mar 100 Dining table: 0.45
Mar 100 Dog: 0.0
Mar 100 Donut: -1.0
Mar 100 Elephant: 0.5111
Mar 100 Fire hydrant: -1.0
Mar 100 Fork: 0.0643
Mar 100 Frisbee: 0.6333
Mar 100 Giraffe: 0.9556
Mar 100 Handbag: 0.136
Mar 100 Horse: 0.6
Mar 100 Hot dog: 0.0
Mar 100 Keyboard: 0.0
Mar 100 Kite: 0.1611
Mar 100 Knife: 0.0333
Mar 100 Laptop: 0.1222
Mar 100 Microwave: 0.0
Mar 100 Motorcycle: 0.1437
Mar 100 Mouse: 0.0
Mar 100 Orange: 0.0
Mar 100 Oven: 0.0
Mar 100 Parking meter: 0.0
Mar 100 Person: 0.4451
Mar 100 Pizza: 0.16
Mar 100 Potted plant: 0.205
Mar 100 Refrigerator: 0.1286
Mar 100 Remote: 0.0905
Mar 100 Sandwich: 0.0
Mar 100 Scissors: 0.0
Mar 100 Sheep: 0.0
Mar 100 Sink: 0.05
Mar 100 Skateboard: 0.0
Mar 100 Skis: 0.0875
Mar 100 Snowboard: 0.0
Mar 100 Spoon: 0.0
Mar 100 Sports ball: 0.0
Mar 100 Stop sign: 0.225
Mar 100 Suitcase: 0.225
Mar 100 Surfboard: 0.3818
Mar 100 Teddy bear: 0.0
Mar 100 Tennis racket: 0.5
Mar 100 Tie: 0.0
Mar 100 Toaster: -1.0
Mar 100 Toilet: 0.2529
Mar 100 Toothbrush: 0.0
Mar 100 Traffic light: 0.1083
Mar 100 Train: 0.1111
Mar 100 Truck: 0.569
Mar 100 Tv: 0.5615
Mar 100 Umbrella: 0.0353
Mar 100 Vase: 0.0833
Mar 100 Wine glass: 0.0
Mar 100 Zebra: 0.0909
Mar Large: 0.2753
Mar Medium: 0.1135
Mar Small: 0.0232

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 300
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Airplane	Map Apple	Map Backpack	Map Banana	Map Baseball bat	Map Baseball glove	Map Bear	Map Bed	Map Bench	Map Bicycle	Map Bird	Map Boat	Map Book	Map Bottle	Map Bowl	Map Broccoli	Map Bus	Map Cake	Map Car	Map Carrot	Map Cat	Map Cell phone	Map Chair	Map Clock	Map Couch	Map Cow	Map Cup	Map Dining table	Map Dog	Map Donut	Map Elephant	Map Fire hydrant	Map Fork	Map Frisbee	Map Giraffe	Map Handbag	Map Horse	Map Hot dog	Map Keyboard	Map Kite	Map Knife	Map Laptop	Map Large	Map Medium	Map Microwave	Map Motorcycle	Map Mouse	Map Orange	Map Oven	Map Parking meter	Map Person	Map Pizza	Map Potted plant	Map Refrigerator	Map Remote	Map Sandwich	Map Scissors	Map Sheep	Map Sink	Map Skateboard	Map Skis	Map Small	Map Snowboard	Map Spoon	Map Sports ball	Map Stop sign	Map Suitcase	Map Surfboard	Map Teddy bear	Map Tennis racket	Map Tie	Map Toaster	Map Toilet	Map Toothbrush	Map Traffic light	Map Train	Map Truck	Map Tv	Map Umbrella	Map Vase	Map Wine glass	Map Zebra	Mar 1	Mar 10	Mar 100	Mar 100 Airplane	Mar 100 Apple	Mar 100 Backpack	Mar 100 Banana	Mar 100 Baseball bat	Mar 100 Baseball glove	Mar 100 Bear	Mar 100 Bed	Mar 100 Bench	Mar 100 Bicycle	Mar 100 Bird	Mar 100 Boat	Mar 100 Book	Mar 100 Bottle	Mar 100 Bowl	Mar 100 Broccoli	Mar 100 Bus	Mar 100 Cake	Mar 100 Car	Mar 100 Carrot	Mar 100 Cat	Mar 100 Cell phone	Mar 100 Chair	Mar 100 Clock	Mar 100 Couch	Mar 100 Cow	Mar 100 Cup	Mar 100 Dining table	Mar 100 Dog	Mar 100 Donut	Mar 100 Elephant	Mar 100 Fire hydrant	Mar 100 Fork	Mar 100 Frisbee	Mar 100 Giraffe	Mar 100 Handbag	Mar 100 Horse	Mar 100 Hot dog	Mar 100 Keyboard	Mar 100 Kite	Mar 100 Knife	Mar 100 Laptop	Mar 100 Microwave	Mar 100 Motorcycle	Mar 100 Mouse	Mar 100 Orange	Mar 100 Oven	Mar 100 Parking meter	Mar 100 Person	Mar 100 Pizza	Mar 100 Potted plant	Mar 100 Refrigerator	Mar 100 Remote	Mar 100 Sandwich	Mar 100 Scissors	Mar 100 Sheep	Mar 100 Sink	Mar 100 Skateboard	Mar 100 Skis	Mar 100 Snowboard	Mar 100 Spoon	Mar 100 Sports ball	Mar 100 Stop sign	Mar 100 Suitcase	Mar 100 Surfboard	Mar 100 Teddy bear	Mar 100 Tennis racket	Mar 100 Tie	Mar 100 Toaster	Mar 100 Toilet	Mar 100 Toothbrush	Mar 100 Traffic light	Mar 100 Train	Mar 100 Truck	Mar 100 Tv	Mar 100 Umbrella	Mar 100 Vase	Mar 100 Wine glass	Mar 100 Zebra	Mar Large	Mar Medium	Mar Small
76.1385	1.0	740	3.0118	0.0299	0.0372	0.0329	0.0221	0.0	0.0021	0.0063	0.0	0.1984	0.0	0.0	0.0009	0.0	0.0016	0.0123	0.0002	0.046	0.0588	0.0007	0.3589	0.0142	0.1625	0.0	0.0047	0.08	0.0741	0.001	0.0125	0.1326	0.0592	0.0596	0.0	-1.0	0.0034	-1.0	0.0027	0.0008	0.0976	0.0022	0.0022	0.0	0.0	0.008	0.001	0.0009	0.0435	0.0369	0.0	0.0006	0.0	0.0	0.0	0.0	0.1633	0.0451	0.0026	0.0013	0.0053	0.0	0.0	0.0	0.0001	0.0	0.0049	0.0085	0.0	0.0	0.0	0.006	0.0009	0.0027	0.0	0.1978	0.0	-1.0	0.0101	0.0	0.0388	0.0096	0.108	0.2457	0.0001	0.0002	0.0	0.0015	0.1037	0.1704	0.1887	0.6857	0.0	0.0333	0.0545	0.0	0.3438	0.0	0.0	0.275	0.0	0.5	0.2408	0.0344	0.3444	0.224	0.1143	0.7917	0.25	0.3451	0.0	0.3692	0.1474	0.2458	0.1412	0.3455	0.4625	0.2686	0.45	0.0	-1.0	0.5111	-1.0	0.0643	0.6333	0.9556	0.136	0.6	0.0	0.0	0.1611	0.0333	0.1222	0.0	0.1437	0.0	0.0	0.0	0.0	0.4451	0.16	0.205	0.1286	0.0905	0.0	0.0	0.0	0.05	0.0	0.0875	0.0	0.0	0.0	0.225	0.225	0.3818	0.0	0.5	0.0	-1.0	0.2529	0.0	0.1083	0.1111	0.569	0.5615	0.0353	0.0833	0.0	0.0909	0.2753	0.1135	0.0232

Framework versions

Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.4.2
Tokenizers 0.22.2

Task: Object Detection
Library: transformers
Type: d_fine
License: apache-2.0
Created: Jan 13, 2026
Updated: Jan 14, 2026