Update README file

README.md

@@ -3,197 +3,76 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-- **Developed by:** [More Information Needed]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-## Citation [optional]
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## Model Card Authors [optional]
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-## Model Card Contact
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+## Original result
+```
+IoU metric: bbox
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.042
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.058
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.041
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.128
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.093
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.062
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.250
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.470
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.393
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.561
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
+```
+
+## After training result
+```
+IoU metric: bbox
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.575
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.744
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.661
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.534
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.767
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.200
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.648
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.694
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.574
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.835
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
+```
+
+## Config
+- dataset: NIH
+- original model: facebook/detr-resnet-50
+- lr: 1e-05
+- dropout_rate: 0.1
+- weight_decay: 0.05
+- max_epochs: 20
+- train samples: 61
+
+## Logging
+### Training process
+```
+{'validation_loss': tensor(2.8403, device='cuda:0'), 'validation_loss_ce': tensor(0.7536, device='cuda:0'), 'validation_loss_bbox': tensor(0.1414, device='cuda:0'), 'validation_loss_giou': tensor(0.6899, device='cuda:0'), 'validation_cardinality_error': tensor(88.5000, device='cuda:0')}
+{'training_loss': tensor(0.9331, device='cuda:0'), 'train_loss_ce': tensor(0.7954, device='cuda:0'), 'train_loss_bbox': tensor(0.0169, device='cuda:0'), 'train_loss_giou': tensor(0.0266, device='cuda:0'), 'train_cardinality_error': tensor(73., device='cuda:0'), 'validation_loss': tensor(1.9357, device='cuda:0'), 'validation_loss_ce': tensor(0.7015, device='cuda:0'), 'validation_loss_bbox': tensor(0.0786, device='cuda:0'), 'validation_loss_giou': tensor(0.4205, device='cuda:0'), 'validation_cardinality_error': tensor(63., device='cuda:0')}
+{'training_loss': tensor(0.7740, device='cuda:0'), 'train_loss_ce': tensor(0.6548, device='cuda:0'), 'train_loss_bbox': tensor(0.0032, device='cuda:0'), 'train_loss_giou': tensor(0.0516, device='cuda:0'), 'train_cardinality_error': tensor(15., device='cuda:0'), 'validation_loss': tensor(1.6569, device='cuda:0'), 'validation_loss_ce': tensor(0.6407, device='cuda:0'), 'validation_loss_bbox': tensor(0.0773, device='cuda:0'), 'validation_loss_giou': tensor(0.3149, device='cuda:0'), 'validation_cardinality_error': tensor(38.3846, device='cuda:0')}
+{'training_loss': tensor(0.8202, device='cuda:0'), 'train_loss_ce': tensor(0.5803, device='cuda:0'), 'train_loss_bbox': tensor(0.0250, device='cuda:0'), 'train_loss_giou': tensor(0.0574, device='cuda:0'), 'train_cardinality_error': tensor(19., device='cuda:0'), 'validation_loss': tensor(1.5251, device='cuda:0'), 'validation_loss_ce': tensor(0.6084, device='cuda:0'), 'validation_loss_bbox': tensor(0.0518, device='cuda:0'), 'validation_loss_giou': tensor(0.3288, device='cuda:0'), 'validation_cardinality_error': tensor(23.6154, device='cuda:0')}
+{'training_loss': tensor(0.6044, device='cuda:0'), 'train_loss_ce': tensor(0.4874, device='cuda:0'), 'train_loss_bbox': tensor(0.0041, device='cuda:0'), 'train_loss_giou': tensor(0.0483, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.6047, device='cuda:0'), 'validation_loss_ce': tensor(0.5633, device='cuda:0'), 'validation_loss_bbox': tensor(0.0684, device='cuda:0'), 'validation_loss_giou': tensor(0.3497, device='cuda:0'), 'validation_cardinality_error': tensor(13.2308, device='cuda:0')}
+{'training_loss': tensor(0.6582, device='cuda:0'), 'train_loss_ce': tensor(0.5104, device='cuda:0'), 'train_loss_bbox': tensor(0.0069, device='cuda:0'), 'train_loss_giou': tensor(0.0567, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.3342, device='cuda:0'), 'validation_loss_ce': tensor(0.5352, device='cuda:0'), 'validation_loss_bbox': tensor(0.0504, device='cuda:0'), 'validation_loss_giou': tensor(0.2735, device='cuda:0'), 'validation_cardinality_error': tensor(8.1538, device='cuda:0')}
+{'training_loss': tensor(1.0112, device='cuda:0'), 'train_loss_ce': tensor(0.5257, device='cuda:0'), 'train_loss_bbox': tensor(0.0471, device='cuda:0'), 'train_loss_giou': tensor(0.1252, device='cuda:0'), 'train_cardinality_error': tensor(3., device='cuda:0'), 'validation_loss': tensor(1.2920, device='cuda:0'), 'validation_loss_ce': tensor(0.5065, device='cuda:0'), 'validation_loss_bbox': tensor(0.0475, device='cuda:0'), 'validation_loss_giou': tensor(0.2741, device='cuda:0'), 'validation_cardinality_error': tensor(5.1538, device='cuda:0')}
+{'training_loss': tensor(0.4205, device='cuda:0'), 'train_loss_ce': tensor(0.3367, device='cuda:0'), 'train_loss_bbox': tensor(0.0080, device='cuda:0'), 'train_loss_giou': tensor(0.0220, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.6009, device='cuda:0'), 'validation_loss_ce': tensor(0.4899, device='cuda:0'), 'validation_loss_bbox': tensor(0.0742, device='cuda:0'), 'validation_loss_giou': tensor(0.3700, device='cuda:0'), 'validation_cardinality_error': tensor(3.4615, device='cuda:0')}
+{'training_loss': tensor(0.4747, device='cuda:0'), 'train_loss_ce': tensor(0.3562, device='cuda:0'), 'train_loss_bbox': tensor(0.0168, device='cuda:0'), 'train_loss_giou': tensor(0.0172, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.3055, device='cuda:0'), 'validation_loss_ce': tensor(0.4662, device='cuda:0'), 'validation_loss_bbox': tensor(0.0488, device='cuda:0'), 'validation_loss_giou': tensor(0.2977, device='cuda:0'), 'validation_cardinality_error': tensor(2.4615, device='cuda:0')}
+{'training_loss': tensor(0.6444, device='cuda:0'), 'train_loss_ce': tensor(0.4712, device='cuda:0'), 'train_loss_bbox': tensor(0.0081, device='cuda:0'), 'train_loss_giou': tensor(0.0665, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.4009, device='cuda:0'), 'validation_loss_ce': tensor(0.4472, device='cuda:0'), 'validation_loss_bbox': tensor(0.0580, device='cuda:0'), 'validation_loss_giou': tensor(0.3319, device='cuda:0'), 'validation_cardinality_error': tensor(1.6923, device='cuda:0')}
+{'training_loss': tensor(0.3142, device='cuda:0'), 'train_loss_ce': tensor(0.2558, device='cuda:0'), 'train_loss_bbox': tensor(0.0038, device='cuda:0'), 'train_loss_giou': tensor(0.0198, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.2037, device='cuda:0'), 'validation_loss_ce': tensor(0.4325, device='cuda:0'), 'validation_loss_bbox': tensor(0.0478, device='cuda:0'), 'validation_loss_giou': tensor(0.2662, device='cuda:0'), 'validation_cardinality_error': tensor(1.7692, device='cuda:0')}
+{'training_loss': tensor(1.2118, device='cuda:0'), 'train_loss_ce': tensor(0.5910, device='cuda:0'), 'train_loss_bbox': tensor(0.0650, device='cuda:0'), 'train_loss_giou': tensor(0.1480, device='cuda:0'), 'train_cardinality_error': tensor(6., device='cuda:0'), 'validation_loss': tensor(1.3762, device='cuda:0'), 'validation_loss_ce': tensor(0.4274, device='cuda:0'), 'validation_loss_bbox': tensor(0.0517, device='cuda:0'), 'validation_loss_giou': tensor(0.3451, device='cuda:0'), 'validation_cardinality_error': tensor(1.5385, device='cuda:0')}
+{'training_loss': tensor(0.3037, device='cuda:0'), 'train_loss_ce': tensor(0.2012, device='cuda:0'), 'train_loss_bbox': tensor(0.0025, device='cuda:0'), 'train_loss_giou': tensor(0.0449, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.2914, device='cuda:0'), 'validation_loss_ce': tensor(0.4120, device='cuda:0'), 'validation_loss_bbox': tensor(0.0510, device='cuda:0'), 'validation_loss_giou': tensor(0.3121, device='cuda:0'), 'validation_cardinality_error': tensor(1.4615, device='cuda:0')}
+{'training_loss': tensor(0.3875, device='cuda:0'), 'train_loss_ce': tensor(0.2326, device='cuda:0'), 'train_loss_bbox': tensor(0.0093, device='cuda:0'), 'train_loss_giou': tensor(0.0543, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.5544, device='cuda:0'), 'validation_loss_ce': tensor(0.3982, device='cuda:0'), 'validation_loss_bbox': tensor(0.0771, device='cuda:0'), 'validation_loss_giou': tensor(0.3854, device='cuda:0'), 'validation_cardinality_error': tensor(1.3846, device='cuda:0')}
+{'training_loss': tensor(2.0364, device='cuda:0'), 'train_loss_ce': tensor(0.3892, device='cuda:0'), 'train_loss_bbox': tensor(0.2506, device='cuda:0'), 'train_loss_giou': tensor(0.1970, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.4121, device='cuda:0'), 'validation_loss_ce': tensor(0.3892, device='cuda:0'), 'validation_loss_bbox': tensor(0.0629, device='cuda:0'), 'validation_loss_giou': tensor(0.3542, device='cuda:0'), 'validation_cardinality_error': tensor(1.2308, device='cuda:0')}
+{'training_loss': tensor(0.3154, device='cuda:0'), 'train_loss_ce': tensor(0.2601, device='cuda:0'), 'train_loss_bbox': tensor(0.0058, device='cuda:0'), 'train_loss_giou': tensor(0.0131, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.1014, device='cuda:0'), 'validation_loss_ce': tensor(0.3505, device='cuda:0'), 'validation_loss_bbox': tensor(0.0466, device='cuda:0'), 'validation_loss_giou': tensor(0.2590, device='cuda:0'), 'validation_cardinality_error': tensor(1.0769, device='cuda:0')}
+{'training_loss': tensor(0.3392, device='cuda:0'), 'train_loss_ce': tensor(0.1534, device='cuda:0'), 'train_loss_bbox': tensor(0.0219, device='cuda:0'), 'train_loss_giou': tensor(0.0381, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.1544, device='cuda:0'), 'validation_loss_ce': tensor(0.3387, device='cuda:0'), 'validation_loss_bbox': tensor(0.0510, device='cuda:0'), 'validation_loss_giou': tensor(0.2803, device='cuda:0'), 'validation_cardinality_error': tensor(0.9231, device='cuda:0')}
+{'training_loss': tensor(0.3263, device='cuda:0'), 'train_loss_ce': tensor(0.2588, device='cuda:0'), 'train_loss_bbox': tensor(0.0077, device='cuda:0'), 'train_loss_giou': tensor(0.0145, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.1032, device='cuda:0'), 'validation_loss_ce': tensor(0.3281, device='cuda:0'), 'validation_loss_bbox': tensor(0.0441, device='cuda:0'), 'validation_loss_giou': tensor(0.2773, device='cuda:0'), 'validation_cardinality_error': tensor(0.7692, device='cuda:0')}
+{'training_loss': tensor(0.1587, device='cuda:0'), 'train_loss_ce': tensor(0.1014, device='cuda:0'), 'train_loss_bbox': tensor(0.0073, device='cuda:0'), 'train_loss_giou': tensor(0.0105, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.1960, device='cuda:0'), 'validation_loss_ce': tensor(0.3185, device='cuda:0'), 'validation_loss_bbox': tensor(0.0570, device='cuda:0'), 'validation_loss_giou': tensor(0.2962, device='cuda:0'), 'validation_cardinality_error': tensor(0.9231, device='cuda:0')}
+{'training_loss': tensor(0.2787, device='cuda:0'), 'train_loss_ce': tensor(0.1191, device='cuda:0'), 'train_loss_bbox': tensor(0.0105, device='cuda:0'), 'train_loss_giou': tensor(0.0536, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.9316, device='cuda:0'), 'validation_loss_ce': tensor(0.2925, device='cuda:0'), 'validation_loss_bbox': tensor(0.0291, device='cuda:0'), 'validation_loss_giou': tensor(0.2469, device='cuda:0'), 'validation_cardinality_error': tensor(1.0769, device='cuda:0')}
+{'training_loss': tensor(0.1896, device='cuda:0'), 'train_loss_ce': tensor(0.0810, device='cuda:0'), 'train_loss_bbox': tensor(0.0107, device='cuda:0'), 'train_loss_giou': tensor(0.0276, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.8570, device='cuda:0'), 'validation_loss_ce': tensor(0.2889, device='cuda:0'), 'validation_loss_bbox': tensor(0.0264, device='cuda:0'), 'validation_loss_giou': tensor(0.2180, device='cuda:0'), 'validation_cardinality_error': tensor(1.1538, device='cuda:0')}
+```
+
+## Examples
+{'size': tensor([ 800, 1066]), 'image_id': tensor([0]), 'class_labels': tensor([0]), 'boxes': tensor([[0.5955, 0.5811, 0.2202, 0.3561]]), 'area': tensor([3681.5083]), 'iscrowd': tensor([0]), 'orig_size': tensor([1536, 2048])}
+
+![Example](./example.png)