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metadata
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
  - generated_from_trainer
model-index:
  - name: detr-resnet-50_finetuned_plant_disease_detection_processed
    results: []

detr-resnet-50_finetuned_plant_disease_detection_processed

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

  • Loss: 0.6402

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: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.2
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
5.2965 0.19 50 4.4784
4.7649 0.38 100 4.3439
4.4907 0.57 150 4.0077
4.3973 0.76 200 3.2143
3.4084 0.95 250 2.6818
2.7091 1.14 300 2.3603
2.4601 1.33 350 1.9004
2.1096 1.52 400 1.5639
1.6941 1.7 450 1.3240
1.4949 1.89 500 1.1247
1.2246 2.08 550 1.0421
1.4479 2.27 600 1.1546
1.1327 2.46 650 1.1098
1.1184 2.65 700 0.8950
1.0516 2.84 750 0.8601
1.2556 3.03 800 0.8575
1.1216 3.22 850 0.8314
1.1027 3.41 900 1.0676
1.0815 3.6 950 0.9716
1.2254 3.79 1000 1.0091
0.9896 3.98 1050 0.7600
1.0736 4.17 1100 0.8907
1.2462 4.36 1150 0.7506
0.9959 4.55 1200 0.7623
1.0895 4.73 1250 0.7570
1.0736 4.92 1300 0.8248
1.1015 5.11 1350 0.8682
1.1423 5.3 1400 0.8340
1.0906 5.49 1450 0.8372
0.9333 5.68 1500 0.8420
1.1347 5.87 1550 0.8718
0.9407 6.06 1600 0.8270
0.8138 6.25 1650 0.8241
0.8731 6.44 1700 0.8013
1.0146 6.63 1750 0.7704
0.8847 6.82 1800 0.8885
1.0283 7.01 1850 0.8804
1.0359 7.2 1900 0.7907
0.987 7.39 1950 0.7997
1.0279 7.58 2000 0.9095
0.9027 7.77 2050 0.6823
0.927 7.95 2100 0.6728
1.0499 8.14 2150 0.6537
0.9774 8.33 2200 0.6455
0.9171 8.52 2250 0.6456
1.0002 8.71 2300 0.6723
0.9052 8.9 2350 0.6554
0.9029 9.09 2400 0.7272
1.0247 9.28 2450 0.6997
0.8296 9.47 2500 0.6661
1.0659 9.66 2550 0.7914
1.0226 9.85 2600 0.7823
0.9419 10.04 2650 0.7709
0.9008 10.23 2700 0.8114
0.826 10.42 2750 0.7042
0.7957 10.61 2800 0.7764
1.0086 10.8 2850 0.8362
1.0076 10.98 2900 0.8048
0.9613 11.17 2950 0.6945
0.9155 11.36 3000 0.7011
0.9436 11.55 3050 0.6524
0.9134 11.74 3100 0.6582
0.817 11.93 3150 0.6678
0.8545 12.12 3200 0.6520
0.9801 12.31 3250 0.7813
0.8566 12.5 3300 0.7205
0.8966 12.69 3350 0.6326
0.8705 12.88 3400 0.6577
0.8193 13.07 3450 0.6391
0.8099 13.26 3500 0.6658
0.921 13.45 3550 0.6535
0.7915 13.64 3600 0.6576
1.1439 13.83 3650 0.6593
0.8702 14.02 3700 0.6519
0.73 14.2 3750 0.6403
0.8306 14.39 3800 0.6393
0.8678 14.58 3850 0.6405
1.0003 14.77 3900 0.6407
1.023 14.96 3950 0.6402

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.15.0