|
--- |
|
license: apache-2.0 |
|
base_model: facebook/detr-resnet-50 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: detr-V8 |
|
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. --> |
|
|
|
# detr-V8 |
|
|
|
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co./facebook/detr-resnet-50) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2139 |
|
|
|
## 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: 2e-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: 50 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:------:|:---------------:| |
|
| No log | 0.48 | 1000 | 0.3770 | |
|
| No log | 0.96 | 2000 | 0.3967 | |
|
| 0.4391 | 1.43 | 3000 | 0.3822 | |
|
| 0.4391 | 1.91 | 4000 | 0.4163 | |
|
| 0.4434 | 2.39 | 5000 | 0.3888 | |
|
| 0.4434 | 2.87 | 6000 | 0.3867 | |
|
| 0.4509 | 3.35 | 7000 | 0.4205 | |
|
| 0.4509 | 3.83 | 8000 | 0.4014 | |
|
| 0.455 | 4.3 | 9000 | 0.4117 | |
|
| 0.455 | 4.78 | 10000 | 0.3964 | |
|
| 0.4476 | 5.26 | 11000 | 0.3915 | |
|
| 0.4476 | 5.74 | 12000 | 0.3919 | |
|
| 0.444 | 6.22 | 13000 | 0.4026 | |
|
| 0.444 | 6.7 | 14000 | 0.3832 | |
|
| 0.443 | 7.17 | 15000 | 0.4057 | |
|
| 0.443 | 7.65 | 16000 | 0.3677 | |
|
| 0.4232 | 8.13 | 17000 | 0.3746 | |
|
| 0.4232 | 8.61 | 18000 | 0.3672 | |
|
| 0.4202 | 9.09 | 19000 | 0.3629 | |
|
| 0.4202 | 9.56 | 20000 | 0.3739 | |
|
| 0.4131 | 10.04 | 21000 | 0.3712 | |
|
| 0.4131 | 10.52 | 22000 | 0.3470 | |
|
| 0.4131 | 11.0 | 23000 | 0.3632 | |
|
| 0.4024 | 11.48 | 24000 | 0.3561 | |
|
| 0.4024 | 11.96 | 25000 | 0.3562 | |
|
| 0.4013 | 12.43 | 26000 | 0.3253 | |
|
| 0.4013 | 12.91 | 27000 | 0.3390 | |
|
| 0.3925 | 13.39 | 28000 | 0.3398 | |
|
| 0.3925 | 13.87 | 29000 | 0.3460 | |
|
| 0.3804 | 14.35 | 30000 | 0.3338 | |
|
| 0.3804 | 14.83 | 31000 | 0.3201 | |
|
| 0.3757 | 15.3 | 32000 | 0.3119 | |
|
| 0.3757 | 15.78 | 33000 | 0.3106 | |
|
| 0.3663 | 16.26 | 34000 | 0.3164 | |
|
| 0.3663 | 16.74 | 35000 | 0.3190 | |
|
| 0.3588 | 17.22 | 36000 | 0.3141 | |
|
| 0.3588 | 17.69 | 37000 | 0.3262 | |
|
| 0.3515 | 18.17 | 38000 | 0.3027 | |
|
| 0.3515 | 18.65 | 39000 | 0.3178 | |
|
| 0.3557 | 19.13 | 40000 | 0.3053 | |
|
| 0.3557 | 19.61 | 41000 | 0.3032 | |
|
| 0.3478 | 20.09 | 42000 | 0.3147 | |
|
| 0.3478 | 20.56 | 43000 | 0.3069 | |
|
| 0.3451 | 21.04 | 44000 | 0.3070 | |
|
| 0.3451 | 21.52 | 45000 | 0.3055 | |
|
| 0.3451 | 22.0 | 46000 | 0.2883 | |
|
| 0.3367 | 22.48 | 47000 | 0.3090 | |
|
| 0.3367 | 22.96 | 48000 | 0.2906 | |
|
| 0.3348 | 23.43 | 49000 | 0.2805 | |
|
| 0.3348 | 23.91 | 50000 | 0.2920 | |
|
| 0.3298 | 24.39 | 51000 | 0.2854 | |
|
| 0.3298 | 24.87 | 52000 | 0.2841 | |
|
| 0.3254 | 25.35 | 53000 | 0.2822 | |
|
| 0.3254 | 25.82 | 54000 | 0.2716 | |
|
| 0.3169 | 26.3 | 55000 | 0.2825 | |
|
| 0.3169 | 26.78 | 56000 | 0.2700 | |
|
| 0.314 | 27.26 | 57000 | 0.2640 | |
|
| 0.314 | 27.74 | 58000 | 0.2728 | |
|
| 0.3047 | 28.22 | 59000 | 0.2654 | |
|
| 0.3047 | 28.69 | 60000 | 0.2691 | |
|
| 0.2999 | 29.17 | 61000 | 0.2601 | |
|
| 0.2999 | 29.65 | 62000 | 0.2607 | |
|
| 0.297 | 30.13 | 63000 | 0.2581 | |
|
| 0.297 | 30.61 | 64000 | 0.2511 | |
|
| 0.2946 | 31.09 | 65000 | 0.2557 | |
|
| 0.2946 | 31.56 | 66000 | 0.2568 | |
|
| 0.2912 | 32.04 | 67000 | 0.2569 | |
|
| 0.2912 | 32.52 | 68000 | 0.2594 | |
|
| 0.2912 | 33.0 | 69000 | 0.2553 | |
|
| 0.2906 | 33.48 | 70000 | 0.2425 | |
|
| 0.2906 | 33.96 | 71000 | 0.2475 | |
|
| 0.2833 | 34.43 | 72000 | 0.2394 | |
|
| 0.2833 | 34.91 | 73000 | 0.2422 | |
|
| 0.278 | 35.39 | 74000 | 0.2403 | |
|
| 0.278 | 35.87 | 75000 | 0.2349 | |
|
| 0.2738 | 36.35 | 76000 | 0.2300 | |
|
| 0.2738 | 36.82 | 77000 | 0.2332 | |
|
| 0.2701 | 37.3 | 78000 | 0.2309 | |
|
| 0.2701 | 37.78 | 79000 | 0.2298 | |
|
| 0.2659 | 38.26 | 80000 | 0.2343 | |
|
| 0.2659 | 38.74 | 81000 | 0.2265 | |
|
| 0.2626 | 39.22 | 82000 | 0.2310 | |
|
| 0.2626 | 39.69 | 83000 | 0.2255 | |
|
| 0.259 | 40.17 | 84000 | 0.2263 | |
|
| 0.259 | 40.65 | 85000 | 0.2282 | |
|
| 0.2563 | 41.13 | 86000 | 0.2309 | |
|
| 0.2563 | 41.61 | 87000 | 0.2270 | |
|
| 0.2548 | 42.09 | 88000 | 0.2237 | |
|
| 0.2548 | 42.56 | 89000 | 0.2203 | |
|
| 0.254 | 43.04 | 90000 | 0.2204 | |
|
| 0.254 | 43.52 | 91000 | 0.2218 | |
|
| 0.254 | 44.0 | 92000 | 0.2207 | |
|
| 0.2484 | 44.48 | 93000 | 0.2144 | |
|
| 0.2484 | 44.95 | 94000 | 0.2194 | |
|
| 0.2475 | 45.43 | 95000 | 0.2165 | |
|
| 0.2475 | 45.91 | 96000 | 0.2162 | |
|
| 0.2453 | 46.39 | 97000 | 0.2136 | |
|
| 0.2453 | 46.87 | 98000 | 0.2152 | |
|
| 0.2441 | 47.35 | 99000 | 0.2162 | |
|
| 0.2441 | 47.82 | 100000 | 0.2171 | |
|
| 0.2408 | 48.3 | 101000 | 0.2119 | |
|
| 0.2408 | 48.78 | 102000 | 0.2131 | |
|
| 0.2389 | 49.26 | 103000 | 0.2109 | |
|
| 0.2389 | 49.74 | 104000 | 0.2139 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.31.0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.13.1 |
|
- Tokenizers 0.13.3 |
|
|
