Edit model card

conditional-detr-resnet-50_adamw_hf_finetuned_food-roboflow

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 10.2661

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: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
4448.7409 0.77 50 2784.9878
2312.7831 1.54 100 1275.4084
1139.5262 2.31 150 825.0410
857.6314 3.08 200 626.2924
630.5604 3.85 250 492.9028
518.8461 4.62 300 391.5704
418.3785 5.38 350 312.8008
331.9224 6.15 400 250.7763
261.2112 6.92 450 201.8681
207.421 7.69 500 163.3361
172.0157 8.46 550 132.3763
140.2059 9.23 600 108.1960
110.3696 10.0 650 89.2497
95.9428 10.77 700 73.2426
78.812 11.54 750 61.4362
64.1493 12.31 800 51.2599
56.184 13.08 850 43.4741
46.7644 13.85 900 37.4028
38.1726 14.62 950 32.6764
34.7277 15.38 1000 28.1207
29.9978 16.15 1050 25.0045
27.5957 16.92 1100 22.3012
23.6549 17.69 1150 19.9766
21.4961 18.46 1200 18.2427
19.3312 19.23 1250 16.8829
17.8215 20.0 1300 15.5794
16.2877 20.77 1350 14.5258
16.0017 21.54 1400 13.5888
14.6977 22.31 1450 13.0312
13.9111 23.08 1500 12.4389
13.5826 23.85 1550 11.8718
12.5621 24.62 1600 11.6370
12.1993 25.38 1650 11.1523
12.2491 26.15 1700 10.8586
11.5879 26.92 1750 10.6795
11.4736 27.69 1800 10.5273
11.5405 28.46 1850 10.4190
11.5894 29.23 1900 10.3225
11.0308 30.0 1950 10.2661

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1
Downloads last month
6
Safetensors
Model size
43.5M params
Tensor type
F32
·

Finetuned from