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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: fb-detr-aug-table_detection_v1.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fb-detr-aug-table_detection_v1.0
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3284
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1127 | 1.21 | 20 | 1.6338 |
| 1.8818 | 2.42 | 40 | 1.0008 |
| 1.6752 | 3.64 | 60 | 1.4500 |
| 1.516 | 4.85 | 80 | 0.9846 |
| 1.328 | 6.06 | 100 | 1.0746 |
| 1.2713 | 7.27 | 120 | 1.1575 |
| 1.1762 | 8.48 | 140 | 0.7001 |
| 1.1547 | 9.7 | 160 | 1.0982 |
| 1.2178 | 10.91 | 180 | 1.2437 |
| 1.0999 | 12.12 | 200 | 0.9853 |
| 1.1452 | 13.33 | 220 | 0.8249 |
| 1.0528 | 14.55 | 240 | 0.7035 |
| 1.0157 | 15.76 | 260 | 0.7584 |
| 0.9898 | 16.97 | 280 | 0.7169 |
| 0.9011 | 18.18 | 300 | 0.9833 |
| 0.9248 | 19.39 | 320 | 0.5799 |
| 0.9295 | 20.61 | 340 | 0.7567 |
| 0.8687 | 21.82 | 360 | 0.8273 |
| 0.9934 | 23.03 | 380 | 0.7053 |
| 0.9039 | 24.24 | 400 | 0.7121 |
| 0.9244 | 25.45 | 420 | 0.7668 |
| 0.8525 | 26.67 | 440 | 0.8034 |
| 0.8996 | 27.88 | 460 | 0.7558 |
| 0.9486 | 29.09 | 480 | 0.6570 |
| 0.9838 | 30.3 | 500 | 0.6775 |
| 1.0131 | 31.52 | 520 | 0.6643 |
| 0.911 | 32.73 | 540 | 0.6673 |
| 0.9749 | 33.94 | 560 | 0.7285 |
| 0.9277 | 35.15 | 580 | 0.5660 |
| 0.885 | 36.36 | 600 | 0.6928 |
| 0.8128 | 37.58 | 620 | 0.6517 |
| 0.8082 | 38.79 | 640 | 0.6254 |
| 0.8702 | 40.0 | 660 | 0.7354 |
| 0.8563 | 41.21 | 680 | 0.6653 |
| 0.8147 | 42.42 | 700 | 0.7279 |
| 0.7741 | 43.64 | 720 | 0.8649 |
| 0.7128 | 44.85 | 740 | 0.6545 |
| 0.7806 | 46.06 | 760 | 0.6264 |
| 0.7497 | 47.27 | 780 | 0.6577 |
| 0.687 | 48.48 | 800 | 0.6218 |
| 0.761 | 49.7 | 820 | 0.8314 |
| 0.7987 | 50.91 | 840 | 0.6444 |
| 0.7357 | 52.12 | 860 | 0.6575 |
| 0.7023 | 53.33 | 880 | 0.5817 |
| 0.6802 | 54.55 | 900 | 0.6244 |
| 0.7285 | 55.76 | 920 | 0.5916 |
| 0.6959 | 56.97 | 940 | 0.5081 |
| 0.6638 | 58.18 | 960 | 0.5037 |
| 0.6957 | 59.39 | 980 | 0.5085 |
| 0.6571 | 60.61 | 1000 | 0.4837 |
| 0.6837 | 61.82 | 1020 | 0.6387 |
| 0.7012 | 63.03 | 1040 | 0.4773 |
| 0.7139 | 64.24 | 1060 | 0.5028 |
| 0.7234 | 65.45 | 1080 | 0.5678 |
| 0.7228 | 66.67 | 1100 | 0.6430 |
| 0.6973 | 67.88 | 1120 | 0.6091 |
| 0.7096 | 69.09 | 1140 | 0.4702 |
| 0.6688 | 70.3 | 1160 | 0.5281 |
| 0.6378 | 71.52 | 1180 | 0.5869 |
| 0.6533 | 72.73 | 1200 | 0.5513 |
| 0.5966 | 73.94 | 1220 | 0.5030 |
| 0.6459 | 75.15 | 1240 | 0.5056 |
| 0.6496 | 76.36 | 1260 | 0.5982 |
| 0.7562 | 77.58 | 1280 | 0.4316 |
| 0.6744 | 78.79 | 1300 | 0.5127 |
| 0.725 | 80.0 | 1320 | 0.4750 |
| 0.6317 | 81.21 | 1340 | 0.5916 |
| 0.6138 | 82.42 | 1360 | 0.5602 |
| 0.5979 | 83.64 | 1380 | 0.5578 |
| 0.6455 | 84.85 | 1400 | 0.5035 |
| 0.6428 | 86.06 | 1420 | 0.4647 |
| 0.6101 | 87.27 | 1440 | 0.5262 |
| 0.6003 | 88.48 | 1460 | 0.4931 |
| 0.6019 | 89.7 | 1480 | 0.4655 |
| 0.609 | 90.91 | 1500 | 0.5081 |
| 0.6059 | 92.12 | 1520 | 0.4959 |
| 0.5952 | 93.33 | 1540 | 0.4069 |
| 0.6115 | 94.55 | 1560 | 0.5783 |
| 0.6277 | 95.76 | 1580 | 0.5889 |
| 0.6392 | 96.97 | 1600 | 0.5349 |
| 0.6003 | 98.18 | 1620 | 0.4729 |
| 0.6195 | 99.39 | 1640 | 0.4943 |
| 0.6209 | 100.61 | 1660 | 0.5134 |
| 0.6042 | 101.82 | 1680 | 0.5111 |
| 0.5964 | 103.03 | 1700 | 0.4301 |
| 0.5716 | 104.24 | 1720 | 0.4129 |
| 0.5466 | 105.45 | 1740 | 0.5458 |
| 0.5679 | 106.67 | 1760 | 0.5224 |
| 0.5754 | 107.88 | 1780 | 0.4612 |
| 0.543 | 109.09 | 1800 | 0.4411 |
| 0.5434 | 110.3 | 1820 | 0.3614 |
| 0.5682 | 111.52 | 1840 | 0.4925 |
| 0.6027 | 112.73 | 1860 | 0.4388 |
| 0.5683 | 113.94 | 1880 | 0.4456 |
| 0.5566 | 115.15 | 1900 | 0.4899 |
| 0.5738 | 116.36 | 1920 | 0.4500 |
| 0.5494 | 117.58 | 1940 | 0.4949 |
| 0.5848 | 118.79 | 1960 | 0.4078 |
| 0.6483 | 120.0 | 1980 | 0.4234 |
| 0.5738 | 121.21 | 2000 | 0.6240 |
| 0.5656 | 122.42 | 2020 | 0.6076 |
| 0.52 | 123.64 | 2040 | 0.4267 |
| 0.5692 | 124.85 | 2060 | 0.4629 |
| 0.5728 | 126.06 | 2080 | 0.4723 |
| 0.6444 | 127.27 | 2100 | 0.4098 |
| 0.565 | 128.48 | 2120 | 0.4331 |
| 0.5484 | 129.7 | 2140 | 0.4324 |
| 0.5164 | 130.91 | 2160 | 0.4289 |
| 0.5354 | 132.12 | 2180 | 0.3927 |
| 0.5332 | 133.33 | 2200 | 0.3951 |
| 0.4956 | 134.55 | 2220 | 0.4877 |
| 0.5107 | 135.76 | 2240 | 0.5421 |
| 0.5192 | 136.97 | 2260 | 0.4340 |
| 0.4702 | 138.18 | 2280 | 0.5052 |
| 0.4863 | 139.39 | 2300 | 0.4147 |
| 0.4977 | 140.61 | 2320 | 0.4434 |
| 0.5222 | 141.82 | 2340 | 0.4550 |
| 0.5292 | 143.03 | 2360 | 0.4839 |
| 0.5376 | 144.24 | 2380 | 0.3728 |
| 0.4915 | 145.45 | 2400 | 0.4733 |
| 0.4641 | 146.67 | 2420 | 0.3470 |
| 0.5144 | 147.88 | 2440 | 0.3606 |
| 0.4891 | 149.09 | 2460 | 0.4212 |
| 0.4758 | 150.3 | 2480 | 0.6014 |
| 0.4901 | 151.52 | 2500 | 0.3525 |
| 0.4809 | 152.73 | 2520 | 0.4205 |
| 0.486 | 153.94 | 2540 | 0.3663 |
| 0.4943 | 155.15 | 2560 | 0.5401 |
| 0.4857 | 156.36 | 2580 | 0.4914 |
| 0.4898 | 157.58 | 2600 | 0.4820 |
| 0.4783 | 158.79 | 2620 | 0.4178 |
| 0.4941 | 160.0 | 2640 | 0.4133 |
| 0.4607 | 161.21 | 2660 | 0.3855 |
| 0.4797 | 162.42 | 2680 | 0.3911 |
| 0.4874 | 163.64 | 2700 | 0.3821 |
| 0.4799 | 164.85 | 2720 | 0.4532 |
| 0.4683 | 166.06 | 2740 | 0.4442 |
| 0.4843 | 167.27 | 2760 | 0.3532 |
| 0.4781 | 168.48 | 2780 | 0.5200 |
| 0.4561 | 169.7 | 2800 | 0.4211 |
| 0.4745 | 170.91 | 2820 | 0.4610 |
| 0.4872 | 172.12 | 2840 | 0.3453 |
| 0.4299 | 173.33 | 2860 | 0.4454 |
| 0.4609 | 174.55 | 2880 | 0.3775 |
| 0.4318 | 175.76 | 2900 | 0.4044 |
| 0.4429 | 176.97 | 2920 | 0.5326 |
| 0.4521 | 178.18 | 2940 | 0.3521 |
| 0.46 | 179.39 | 2960 | 0.4162 |
| 0.4858 | 180.61 | 2980 | 0.4760 |
| 0.4483 | 181.82 | 3000 | 0.3208 |
| 0.4553 | 183.03 | 3020 | 0.3736 |
| 0.4497 | 184.24 | 3040 | 0.3852 |
| 0.4487 | 185.45 | 3060 | 0.4270 |
| 0.4646 | 186.67 | 3080 | 0.4376 |
| 0.4538 | 187.88 | 3100 | 0.4299 |
| 0.4915 | 189.09 | 3120 | 0.2842 |
| 0.4194 | 190.3 | 3140 | 0.4162 |
| 0.4571 | 191.52 | 3160 | 0.4434 |
| 0.4228 | 192.73 | 3180 | 0.6554 |
| 0.4345 | 193.94 | 3200 | 0.2984 |
| 0.4424 | 195.15 | 3220 | 0.3035 |
| 0.4259 | 196.36 | 3240 | 0.4230 |
| 0.4161 | 197.58 | 3260 | 0.2558 |
| 0.405 | 198.79 | 3280 | 0.3711 |
| 0.4385 | 200.0 | 3300 | 0.2988 |
| 0.4034 | 201.21 | 3320 | 0.4759 |
| 0.4203 | 202.42 | 3340 | 0.3641 |
| 0.4559 | 203.64 | 3360 | 0.3186 |
| 0.4457 | 204.85 | 3380 | 0.3593 |
| 0.4072 | 206.06 | 3400 | 0.3301 |
| 0.4254 | 207.27 | 3420 | 0.2779 |
| 0.4153 | 208.48 | 3440 | 0.3963 |
| 0.4259 | 209.7 | 3460 | 0.3817 |
| 0.4273 | 210.91 | 3480 | 0.3069 |
| 0.3945 | 212.12 | 3500 | 0.3477 |
| 0.3849 | 213.33 | 3520 | 0.3495 |
| 0.3944 | 214.55 | 3540 | 0.4825 |
| 0.3881 | 215.76 | 3560 | 0.3790 |
| 0.3856 | 216.97 | 3580 | 0.2898 |
| 0.4108 | 218.18 | 3600 | 0.3521 |
| 0.4194 | 219.39 | 3620 | 0.2938 |
| 0.3683 | 220.61 | 3640 | 0.2290 |
| 0.4111 | 221.82 | 3660 | 0.3704 |
| 0.4078 | 223.03 | 3680 | 0.3231 |
| 0.3852 | 224.24 | 3700 | 0.2568 |
| 0.407 | 225.45 | 3720 | 0.4309 |
| 0.3753 | 226.67 | 3740 | 0.3829 |
| 0.3963 | 227.88 | 3760 | 0.3988 |
| 0.3683 | 229.09 | 3780 | 0.3014 |
| 0.3786 | 230.3 | 3800 | 0.2988 |
| 0.3705 | 231.52 | 3820 | 0.3167 |
| 0.3822 | 232.73 | 3840 | 0.3800 |
| 0.3496 | 233.94 | 3860 | 0.3660 |
| 0.407 | 235.15 | 3880 | 0.3476 |
| 0.3938 | 236.36 | 3900 | 0.3337 |
| 0.3526 | 237.58 | 3920 | 0.3130 |
| 0.3815 | 238.79 | 3940 | 0.2702 |
| 0.3677 | 240.0 | 3960 | 0.3134 |
| 0.4319 | 241.21 | 3980 | 0.3871 |
| 0.401 | 242.42 | 4000 | 0.4471 |
| 0.3538 | 243.64 | 4020 | 0.3134 |
| 0.3605 | 244.85 | 4040 | 0.2553 |
| 0.3585 | 246.06 | 4060 | 0.2506 |
| 0.3879 | 247.27 | 4080 | 0.3194 |
| 0.3638 | 248.48 | 4100 | 0.4381 |
| 0.3649 | 249.7 | 4120 | 0.3818 |
| 0.3529 | 250.91 | 4140 | 0.2432 |
| 0.3841 | 252.12 | 4160 | 0.2769 |
| 0.3755 | 253.33 | 4180 | 0.3376 |
| 0.3504 | 254.55 | 4200 | 0.2689 |
| 0.3653 | 255.76 | 4220 | 0.2874 |
| 0.3614 | 256.97 | 4240 | 0.4095 |
| 0.3909 | 258.18 | 4260 | 0.2556 |
| 0.3547 | 259.39 | 4280 | 0.4043 |
| 0.3613 | 260.61 | 4300 | 0.2781 |
| 0.3268 | 261.82 | 4320 | 0.2558 |
| 0.367 | 263.03 | 4340 | 0.3386 |
| 0.3317 | 264.24 | 4360 | 0.2605 |
| 0.3733 | 265.45 | 4380 | 0.2535 |
| 0.3878 | 266.67 | 4400 | 0.2325 |
| 0.3596 | 267.88 | 4420 | 0.2849 |
| 0.3482 | 269.09 | 4440 | 0.2811 |
| 0.3609 | 270.3 | 4460 | 0.3282 |
| 0.373 | 271.52 | 4480 | 0.4058 |
| 0.3792 | 272.73 | 4500 | 0.2404 |
| 0.3563 | 273.94 | 4520 | 0.3351 |
| 0.3215 | 275.15 | 4540 | 0.4536 |
| 0.3389 | 276.36 | 4560 | 0.4224 |
| 0.354 | 277.58 | 4580 | 0.3298 |
| 0.3616 | 278.79 | 4600 | 0.3443 |
| 0.3629 | 280.0 | 4620 | 0.3889 |
| 0.3443 | 281.21 | 4640 | 0.3653 |
| 0.3407 | 282.42 | 4660 | 0.2257 |
| 0.3178 | 283.64 | 4680 | 0.3924 |
| 0.3364 | 284.85 | 4700 | 0.3184 |
| 0.3356 | 286.06 | 4720 | 0.3177 |
| 0.3711 | 287.27 | 4740 | 0.3729 |
| 0.3422 | 288.48 | 4760 | 0.2495 |
| 0.3375 | 289.7 | 4780 | 0.2142 |
| 0.3271 | 290.91 | 4800 | 0.3284 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.11.0
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