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license: apache-2.0
base_model: microsoft/conditional-detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: msoft_detr_finetuned_cppe5_4
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. -->
# msoft_detr_finetuned_cppe5_4
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2977
- Map: 0.021
- Map 50: 0.0499
- Map 75: 0.0154
- Map Small: 0.0068
- Map Medium: 0.0134
- Map Large: 0.0527
- Mar 1: 0.0707
- Mar 10: 0.1568
- Mar 100: 0.2057
- Mar Small: 0.0544
- Mar Medium: 0.1672
- Mar Large: 0.2741
- Map Coverall: 0.06
- Mar 100 Coverall: 0.5324
- Map Face Shield: 0.0055
- Mar 100 Face Shield: 0.0688
- Map Gloves: 0.0015
- Mar 100 Gloves: 0.0946
- Map Goggles: 0.0083
- Mar 100 Goggles: 0.0639
- Map Mask: 0.03
- Mar 100 Mask: 0.2691
## 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: 7
### 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 Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:|
| No log | 1.0 | 32 | 3.7102 | 0.0005 | 0.0021 | 0.0003 | 0.0001 | 0.0004 | 0.0013 | 0.0026 | 0.0261 | 0.0464 | 0.0019 | 0.0354 | 0.0536 | 0.0016 | 0.1713 | 0.0 | 0.0 | 0.0001 | 0.0123 | 0.0 | 0.0111 | 0.0008 | 0.0375 |
| No log | 2.0 | 64 | 2.7691 | 0.0051 | 0.0161 | 0.0015 | 0.0023 | 0.0065 | 0.0113 | 0.0115 | 0.0494 | 0.0884 | 0.0324 | 0.0666 | 0.1259 | 0.0052 | 0.2228 | 0.0 | 0.0 | 0.0001 | 0.0438 | 0.0007 | 0.0194 | 0.0194 | 0.1559 |
| No log | 3.0 | 96 | 2.5735 | 0.0178 | 0.0407 | 0.0149 | 0.0033 | 0.0102 | 0.0334 | 0.0415 | 0.1076 | 0.1578 | 0.0249 | 0.1105 | 0.2077 | 0.0637 | 0.539 | 0.0 | 0.0 | 0.0002 | 0.0446 | 0.0028 | 0.0222 | 0.0222 | 0.1831 |
| No log | 4.0 | 128 | 2.5173 | 0.0246 | 0.0528 | 0.0225 | 0.0048 | 0.0136 | 0.0308 | 0.0557 | 0.1141 | 0.1519 | 0.027 | 0.1288 | 0.2036 | 0.0974 | 0.5074 | 0.0 | 0.0 | 0.0004 | 0.0692 | 0.0 | 0.0 | 0.0254 | 0.1831 |
| No log | 5.0 | 160 | 2.3440 | 0.0167 | 0.0413 | 0.0111 | 0.0066 | 0.0106 | 0.0438 | 0.0517 | 0.1312 | 0.183 | 0.0472 | 0.1398 | 0.2513 | 0.0437 | 0.5022 | 0.0029 | 0.0312 | 0.0009 | 0.0877 | 0.0074 | 0.0528 | 0.0287 | 0.2412 |
| No log | 6.0 | 192 | 2.3166 | 0.0203 | 0.0471 | 0.0156 | 0.0064 | 0.0131 | 0.05 | 0.0655 | 0.1514 | 0.2036 | 0.0635 | 0.1626 | 0.2709 | 0.059 | 0.5346 | 0.0039 | 0.0521 | 0.0014 | 0.1015 | 0.0085 | 0.0722 | 0.0286 | 0.2574 |
| No log | 7.0 | 224 | 2.2977 | 0.021 | 0.0499 | 0.0154 | 0.0068 | 0.0134 | 0.0527 | 0.0707 | 0.1568 | 0.2057 | 0.0544 | 0.1672 | 0.2741 | 0.06 | 0.5324 | 0.0055 | 0.0688 | 0.0015 | 0.0946 | 0.0083 | 0.0639 | 0.03 | 0.2691 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.5.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
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