Instructions to use LLyq/marco_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLyq/marco_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="LLyq/marco_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("LLyq/marco_model") model = AutoModelForObjectDetection.from_pretrained("LLyq/marco_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 15,455 Bytes
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license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: marco_model
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. -->
# marco_model
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: 0.3925
- Map: 0.7574
- Map 50: 0.929
- Map 75: 0.884
- Map Small: -1.0
- Map Medium: 0.6668
- Map Large: 0.7845
- Mar 1: 0.1077
- Mar 10: 0.7532
- Mar 100: 0.8489
- Mar Small: -1.0
- Mar Medium: 0.8074
- Mar Large: 0.8615
- Map Per Class: -1.0
- Mar 100 Per Class: -1.0
- Classes: 0
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Per Class | Mar 100 Per Class | Classes |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-------------:|:-----------------:|:-------:|
| 1.8485 | 1.0 | 9 | 2.0519 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 1.4965 | 2.0 | 18 | 1.4544 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 1.3577 | 3.0 | 27 | 1.3521 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 1.2729 | 4.0 | 36 | 1.2400 | 0.0077 | 0.0281 | 0.004 | -1.0 | 0.0158 | 0.0058 | 0.0039 | 0.009 | 0.009 | -1.0 | 0.0148 | 0.0073 | -1.0 | -1.0 | 0 |
| 1.1613 | 5.0 | 45 | 1.2042 | 0.0066 | 0.0099 | 0.0099 | -1.0 | 0.0079 | 0.0059 | 0.006 | 0.006 | 0.006 | -1.0 | 0.0148 | 0.0034 | -1.0 | -1.0 | 0 |
| 1.1631 | 6.0 | 54 | 1.1257 | 0.0235 | 0.0297 | 0.0229 | -1.0 | 0.0139 | 0.0274 | 0.0219 | 0.0219 | 0.0219 | -1.0 | 0.0167 | 0.0235 | -1.0 | -1.0 | 0 |
| 1.1196 | 7.0 | 63 | 1.1354 | 0.0231 | 0.0297 | 0.0297 | -1.0 | 0.031 | 0.0234 | 0.0172 | 0.021 | 0.021 | -1.0 | 0.0315 | 0.0179 | -1.0 | -1.0 | 0 |
| 1.123 | 8.0 | 72 | 1.0910 | 0.023 | 0.0297 | 0.0297 | -1.0 | 0.0314 | 0.0168 | 0.0142 | 0.0176 | 0.0176 | -1.0 | 0.0315 | 0.0134 | -1.0 | -1.0 | 0 |
| 1.0342 | 9.0 | 81 | 1.0235 | 0.0576 | 0.0748 | 0.0748 | -1.0 | 0.0601 | 0.0618 | 0.0421 | 0.0622 | 0.0622 | -1.0 | 0.0593 | 0.0631 | -1.0 | -1.0 | 0 |
| 1.0549 | 10.0 | 90 | 1.1559 | 0.0366 | 0.0495 | 0.0495 | -1.0 | 0.0609 | 0.0297 | 0.0155 | 0.0352 | 0.0352 | -1.0 | 0.0593 | 0.0279 | -1.0 | -1.0 | 0 |
| 0.9125 | 11.0 | 99 | 0.9385 | 0.1832 | 0.2508 | 0.2184 | -1.0 | 0.2598 | 0.1648 | 0.0691 | 0.2189 | 0.2253 | -1.0 | 0.3111 | 0.1994 | -1.0 | -1.0 | 0 |
| 0.8747 | 12.0 | 108 | 0.9022 | 0.3659 | 0.5151 | 0.4412 | -1.0 | 0.3361 | 0.394 | 0.0824 | 0.4519 | 0.5056 | -1.0 | 0.5481 | 0.4927 | -1.0 | -1.0 | 0 |
| 0.7734 | 13.0 | 117 | 0.7992 | 0.3737 | 0.5225 | 0.4491 | -1.0 | 0.3209 | 0.3989 | 0.0828 | 0.4468 | 0.4605 | -1.0 | 0.4444 | 0.4654 | -1.0 | -1.0 | 0 |
| 0.9409 | 14.0 | 126 | 0.7235 | 0.474 | 0.6527 | 0.5714 | -1.0 | 0.3893 | 0.5149 | 0.0974 | 0.5451 | 0.5798 | -1.0 | 0.5259 | 0.5961 | -1.0 | -1.0 | 0 |
| 0.6904 | 15.0 | 135 | 0.6478 | 0.5892 | 0.7933 | 0.7054 | -1.0 | 0.4855 | 0.6278 | 0.0948 | 0.6292 | 0.7236 | -1.0 | 0.6778 | 0.7374 | -1.0 | -1.0 | 0 |
| 0.6725 | 16.0 | 144 | 0.5890 | 0.6553 | 0.8521 | 0.7901 | -1.0 | 0.5227 | 0.7068 | 0.0953 | 0.6734 | 0.7725 | -1.0 | 0.687 | 0.7983 | -1.0 | -1.0 | 0 |
| 0.6808 | 17.0 | 153 | 0.5885 | 0.6474 | 0.8739 | 0.7781 | -1.0 | 0.5653 | 0.6778 | 0.0948 | 0.6476 | 0.7867 | -1.0 | 0.7537 | 0.7966 | -1.0 | -1.0 | 0 |
| 0.4056 | 18.0 | 162 | 0.5272 | 0.6694 | 0.8891 | 0.8089 | -1.0 | 0.5952 | 0.697 | 0.0944 | 0.679 | 0.8013 | -1.0 | 0.7741 | 0.8095 | -1.0 | -1.0 | 0 |
| 0.5985 | 19.0 | 171 | 0.5146 | 0.655 | 0.8835 | 0.7829 | -1.0 | 0.5655 | 0.6914 | 0.0966 | 0.6738 | 0.7914 | -1.0 | 0.7537 | 0.8028 | -1.0 | -1.0 | 0 |
| 0.395 | 20.0 | 180 | 0.5012 | 0.6867 | 0.8954 | 0.8308 | -1.0 | 0.5623 | 0.7287 | 0.0987 | 0.6742 | 0.806 | -1.0 | 0.7537 | 0.8218 | -1.0 | -1.0 | 0 |
| 0.5581 | 21.0 | 189 | 0.4747 | 0.7079 | 0.9051 | 0.8362 | -1.0 | 0.5806 | 0.7501 | 0.094 | 0.7 | 0.8227 | -1.0 | 0.7926 | 0.8318 | -1.0 | -1.0 | 0 |
| 0.3999 | 22.0 | 198 | 0.4860 | 0.7029 | 0.9115 | 0.8551 | -1.0 | 0.6319 | 0.7286 | 0.1004 | 0.6948 | 0.812 | -1.0 | 0.7815 | 0.8212 | -1.0 | -1.0 | 0 |
| 0.4602 | 23.0 | 207 | 0.4910 | 0.6976 | 0.8884 | 0.8222 | -1.0 | 0.6346 | 0.7229 | 0.1017 | 0.6983 | 0.8009 | -1.0 | 0.7889 | 0.8045 | -1.0 | -1.0 | 0 |
| 0.4216 | 24.0 | 216 | 0.4817 | 0.7081 | 0.902 | 0.8552 | -1.0 | 0.5966 | 0.7483 | 0.1 | 0.709 | 0.8176 | -1.0 | 0.7796 | 0.8291 | -1.0 | -1.0 | 0 |
| 0.4984 | 25.0 | 225 | 0.4645 | 0.7113 | 0.9192 | 0.8414 | -1.0 | 0.6434 | 0.7322 | 0.1056 | 0.7155 | 0.8004 | -1.0 | 0.7519 | 0.8151 | -1.0 | -1.0 | 0 |
| 0.6411 | 26.0 | 234 | 0.4574 | 0.712 | 0.9219 | 0.8573 | -1.0 | 0.6225 | 0.7431 | 0.1013 | 0.706 | 0.8189 | -1.0 | 0.7944 | 0.8263 | -1.0 | -1.0 | 0 |
| 0.4605 | 27.0 | 243 | 0.4536 | 0.7152 | 0.9188 | 0.8555 | -1.0 | 0.6354 | 0.7416 | 0.103 | 0.7099 | 0.8219 | -1.0 | 0.8019 | 0.8279 | -1.0 | -1.0 | 0 |
| 0.4985 | 28.0 | 252 | 0.4349 | 0.7105 | 0.9045 | 0.8509 | -1.0 | 0.6036 | 0.7465 | 0.1017 | 0.721 | 0.8159 | -1.0 | 0.7407 | 0.8385 | -1.0 | -1.0 | 0 |
| 0.3637 | 29.0 | 261 | 0.4239 | 0.7218 | 0.9085 | 0.8706 | -1.0 | 0.5929 | 0.7671 | 0.1043 | 0.7343 | 0.8176 | -1.0 | 0.7185 | 0.8475 | -1.0 | -1.0 | 0 |
| 0.3068 | 30.0 | 270 | 0.4442 | 0.7149 | 0.9153 | 0.8518 | -1.0 | 0.6273 | 0.7437 | 0.1034 | 0.7202 | 0.8227 | -1.0 | 0.7926 | 0.8318 | -1.0 | -1.0 | 0 |
| 0.3456 | 31.0 | 279 | 0.4668 | 0.7161 | 0.9101 | 0.8587 | -1.0 | 0.6638 | 0.7374 | 0.1039 | 0.7189 | 0.8129 | -1.0 | 0.7722 | 0.8251 | -1.0 | -1.0 | 0 |
| 0.437 | 32.0 | 288 | 0.4049 | 0.7589 | 0.9395 | 0.8984 | -1.0 | 0.6825 | 0.7851 | 0.1034 | 0.7391 | 0.8455 | -1.0 | 0.8037 | 0.8581 | -1.0 | -1.0 | 0 |
| 0.3444 | 33.0 | 297 | 0.4229 | 0.747 | 0.9414 | 0.8875 | -1.0 | 0.6868 | 0.7668 | 0.1043 | 0.7266 | 0.8288 | -1.0 | 0.7889 | 0.8408 | -1.0 | -1.0 | 0 |
| 0.3523 | 34.0 | 306 | 0.4203 | 0.7474 | 0.9398 | 0.8744 | -1.0 | 0.6821 | 0.7687 | 0.1077 | 0.7343 | 0.8305 | -1.0 | 0.7963 | 0.8408 | -1.0 | -1.0 | 0 |
| 0.2629 | 35.0 | 315 | 0.4216 | 0.7449 | 0.928 | 0.8813 | -1.0 | 0.6819 | 0.7653 | 0.1094 | 0.7361 | 0.8313 | -1.0 | 0.7778 | 0.8475 | -1.0 | -1.0 | 0 |
| 0.3533 | 36.0 | 324 | 0.4698 | 0.7009 | 0.8817 | 0.8321 | -1.0 | 0.638 | 0.7228 | 0.0974 | 0.709 | 0.8185 | -1.0 | 0.7926 | 0.8263 | -1.0 | -1.0 | 0 |
| 0.2833 | 37.0 | 333 | 0.4804 | 0.7025 | 0.8904 | 0.8546 | -1.0 | 0.6607 | 0.718 | 0.0996 | 0.6983 | 0.8137 | -1.0 | 0.7944 | 0.8196 | -1.0 | -1.0 | 0 |
| 0.3656 | 38.0 | 342 | 0.4546 | 0.7221 | 0.9065 | 0.8543 | -1.0 | 0.6382 | 0.7488 | 0.1039 | 0.727 | 0.8193 | -1.0 | 0.7593 | 0.8374 | -1.0 | -1.0 | 0 |
| 0.4347 | 39.0 | 351 | 0.4096 | 0.7362 | 0.905 | 0.8617 | -1.0 | 0.6388 | 0.7679 | 0.112 | 0.7365 | 0.8343 | -1.0 | 0.7667 | 0.8547 | -1.0 | -1.0 | 0 |
| 0.3386 | 40.0 | 360 | 0.4118 | 0.7517 | 0.9218 | 0.8883 | -1.0 | 0.6665 | 0.777 | 0.1021 | 0.7416 | 0.8468 | -1.0 | 0.8019 | 0.8603 | -1.0 | -1.0 | 0 |
| 0.2718 | 41.0 | 369 | 0.4035 | 0.7532 | 0.9305 | 0.8954 | -1.0 | 0.6756 | 0.7727 | 0.109 | 0.7425 | 0.8429 | -1.0 | 0.8093 | 0.8531 | -1.0 | -1.0 | 0 |
| 0.2767 | 42.0 | 378 | 0.4029 | 0.7598 | 0.9324 | 0.8973 | -1.0 | 0.6522 | 0.7911 | 0.1112 | 0.7442 | 0.8446 | -1.0 | 0.7796 | 0.8642 | -1.0 | -1.0 | 0 |
| 0.3407 | 43.0 | 387 | 0.4037 | 0.7574 | 0.9348 | 0.8848 | -1.0 | 0.6612 | 0.7855 | 0.1086 | 0.7416 | 0.8433 | -1.0 | 0.7852 | 0.8609 | -1.0 | -1.0 | 0 |
| 0.2914 | 44.0 | 396 | 0.3938 | 0.7646 | 0.9385 | 0.9041 | -1.0 | 0.6736 | 0.7913 | 0.109 | 0.7455 | 0.8442 | -1.0 | 0.787 | 0.8615 | -1.0 | -1.0 | 0 |
| 0.2816 | 45.0 | 405 | 0.3961 | 0.7667 | 0.9377 | 0.8954 | -1.0 | 0.671 | 0.794 | 0.1064 | 0.7519 | 0.8476 | -1.0 | 0.7963 | 0.8631 | -1.0 | -1.0 | 0 |
| 0.2724 | 46.0 | 414 | 0.4053 | 0.754 | 0.9263 | 0.8842 | -1.0 | 0.6651 | 0.7787 | 0.106 | 0.7472 | 0.8378 | -1.0 | 0.7778 | 0.8559 | -1.0 | -1.0 | 0 |
| 0.2173 | 47.0 | 423 | 0.4031 | 0.7515 | 0.9222 | 0.879 | -1.0 | 0.653 | 0.7808 | 0.106 | 0.7442 | 0.8395 | -1.0 | 0.7815 | 0.857 | -1.0 | -1.0 | 0 |
| 0.3299 | 48.0 | 432 | 0.4073 | 0.7563 | 0.9291 | 0.8775 | -1.0 | 0.662 | 0.7839 | 0.1069 | 0.7498 | 0.8442 | -1.0 | 0.7963 | 0.8587 | -1.0 | -1.0 | 0 |
| 0.326 | 49.0 | 441 | 0.3962 | 0.7601 | 0.929 | 0.8834 | -1.0 | 0.6728 | 0.7868 | 0.1073 | 0.7515 | 0.8515 | -1.0 | 0.8093 | 0.8642 | -1.0 | -1.0 | 0 |
| 0.267 | 50.0 | 450 | 0.3949 | 0.7592 | 0.9284 | 0.8917 | -1.0 | 0.6737 | 0.7834 | 0.1064 | 0.7506 | 0.8506 | -1.0 | 0.8111 | 0.8626 | -1.0 | -1.0 | 0 |
| 0.3073 | 51.0 | 459 | 0.3979 | 0.754 | 0.9278 | 0.8833 | -1.0 | 0.6652 | 0.7805 | 0.1064 | 0.7451 | 0.8451 | -1.0 | 0.8056 | 0.857 | -1.0 | -1.0 | 0 |
| 0.3166 | 52.0 | 468 | 0.3995 | 0.7545 | 0.9279 | 0.8822 | -1.0 | 0.6645 | 0.78 | 0.106 | 0.7446 | 0.8446 | -1.0 | 0.8074 | 0.8559 | -1.0 | -1.0 | 0 |
| 0.2635 | 53.0 | 477 | 0.3957 | 0.7554 | 0.9279 | 0.8826 | -1.0 | 0.6605 | 0.7836 | 0.1073 | 0.7468 | 0.8464 | -1.0 | 0.8037 | 0.8592 | -1.0 | -1.0 | 0 |
| 0.2663 | 54.0 | 486 | 0.3942 | 0.7556 | 0.9273 | 0.8819 | -1.0 | 0.6625 | 0.7831 | 0.1073 | 0.7524 | 0.8476 | -1.0 | 0.8037 | 0.8609 | -1.0 | -1.0 | 0 |
| 0.3247 | 55.0 | 495 | 0.3940 | 0.7574 | 0.9272 | 0.8819 | -1.0 | 0.6688 | 0.7832 | 0.1073 | 0.7536 | 0.8494 | -1.0 | 0.8093 | 0.8615 | -1.0 | -1.0 | 0 |
| 0.2399 | 56.0 | 504 | 0.3933 | 0.7575 | 0.9275 | 0.8825 | -1.0 | 0.6671 | 0.7836 | 0.1073 | 0.7532 | 0.8494 | -1.0 | 0.8093 | 0.8615 | -1.0 | -1.0 | 0 |
| 0.3087 | 57.0 | 513 | 0.3929 | 0.7591 | 0.929 | 0.8839 | -1.0 | 0.6682 | 0.7855 | 0.1077 | 0.7536 | 0.8498 | -1.0 | 0.8093 | 0.862 | -1.0 | -1.0 | 0 |
| 0.2753 | 58.0 | 522 | 0.3926 | 0.7575 | 0.929 | 0.884 | -1.0 | 0.6667 | 0.7843 | 0.1077 | 0.7532 | 0.8489 | -1.0 | 0.8074 | 0.8615 | -1.0 | -1.0 | 0 |
| 0.2291 | 59.0 | 531 | 0.3925 | 0.7574 | 0.929 | 0.8839 | -1.0 | 0.6667 | 0.7845 | 0.1077 | 0.7532 | 0.8489 | -1.0 | 0.8074 | 0.8615 | -1.0 | -1.0 | 0 |
| 0.2809 | 60.0 | 540 | 0.3925 | 0.7574 | 0.929 | 0.884 | -1.0 | 0.6668 | 0.7845 | 0.1077 | 0.7532 | 0.8489 | -1.0 | 0.8074 | 0.8615 | -1.0 | -1.0 | 0 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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