Add config from convert_rt_detr_original_pytorch_checkpoint_to_pytorch.py
Browse files- README.md +199 -0
- config.json +250 -0
README.md
ADDED
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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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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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [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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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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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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[More Information Needed]
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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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[More Information Needed]
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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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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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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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[More Information Needed]
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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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[More Information Needed]
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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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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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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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[More Information Needed]
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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]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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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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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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config.json
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{
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"activation_dropout": 0.0,
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"activation_function": "silu",
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"anchor_image_size": [
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640,
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640
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],
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"architectures": [
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"RTDetrForObjectDetection"
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],
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"attention_dropout": 0.0,
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"auxiliary_loss": true,
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"backbone": "resnet34d",
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"backbone_config": null,
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"backbone_kwargs": {
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"features_only": true,
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"out_indices": [
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2,
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3,
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4
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]
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},
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"batch_norm_eps": 1e-05,
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"box_noise_scale": 1.0,
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"d_model": 256,
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"decoder_activation_function": "relu",
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"decoder_attention_heads": 8,
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"decoder_ffn_dim": 1024,
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"decoder_in_channels": [
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256,
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256,
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256
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],
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"decoder_layers": 4,
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"decoder_n_points": 4,
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"disable_custom_kernels": true,
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"dropout": 0.0,
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"encode_proj_layers": [
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2
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],
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"encoder_activation_function": "gelu",
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"encoder_attention_heads": 8,
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"encoder_ffn_dim": 1024,
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"encoder_hidden_dim": 256,
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"encoder_in_channels": [
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128,
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256,
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512
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],
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"encoder_layers": 1,
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"eos_coefficient": 0.0001,
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"eval_size": null,
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"feat_strides": [
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8,
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16,
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32
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],
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"focal_loss_alpha": 0.75,
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"focal_loss_gamma": 2.0,
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"hidden_expansion": 0.5,
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"id2label": {
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"0": "person",
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"1": "bicycle",
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"2": "car",
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"3": "motorbike",
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"4": "aeroplane",
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"5": "bus",
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"6": "train",
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"7": "truck",
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"8": "boat",
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"9": "traffic light",
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"10": "fire hydrant",
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"11": "stop sign",
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"12": "parking meter",
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"13": "bench",
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"14": "bird",
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"15": "cat",
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"16": "dog",
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"17": "horse",
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"18": "sheep",
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"19": "cow",
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"20": "elephant",
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"21": "bear",
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"22": "zebra",
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"23": "giraffe",
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"24": "backpack",
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"25": "umbrella",
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"26": "handbag",
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"27": "tie",
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"28": "suitcase",
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"29": "frisbee",
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"30": "skis",
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"31": "snowboard",
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"32": "sports ball",
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"33": "kite",
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"34": "baseball bat",
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"35": "baseball glove",
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"36": "skateboard",
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"37": "surfboard",
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"38": "tennis racket",
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"39": "bottle",
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"40": "wine glass",
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"41": "cup",
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"42": "fork",
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"43": "knife",
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"44": "spoon",
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"45": "bowl",
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"46": "banana",
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"47": "apple",
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"48": "sandwich",
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"49": "orange",
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"50": "broccoli",
|
113 |
+
"51": "carrot",
|
114 |
+
"52": "hot dog",
|
115 |
+
"53": "pizza",
|
116 |
+
"54": "donut",
|
117 |
+
"55": "cake",
|
118 |
+
"56": "chair",
|
119 |
+
"57": "sofa",
|
120 |
+
"58": "pottedplant",
|
121 |
+
"59": "bed",
|
122 |
+
"60": "diningtable",
|
123 |
+
"61": "toilet",
|
124 |
+
"62": "tvmonitor",
|
125 |
+
"63": "laptop",
|
126 |
+
"64": "mouse",
|
127 |
+
"65": "remote",
|
128 |
+
"66": "keyboard",
|
129 |
+
"67": "cell phone",
|
130 |
+
"68": "microwave",
|
131 |
+
"69": "oven",
|
132 |
+
"70": "toaster",
|
133 |
+
"71": "sink",
|
134 |
+
"72": "refrigerator",
|
135 |
+
"73": "book",
|
136 |
+
"74": "clock",
|
137 |
+
"75": "vase",
|
138 |
+
"76": "scissors",
|
139 |
+
"77": "teddy bear",
|
140 |
+
"78": "hair drier",
|
141 |
+
"79": "toothbrush"
|
142 |
+
},
|
143 |
+
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|
144 |
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|
145 |
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|
146 |
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|
147 |
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|
148 |
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|
149 |
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|
150 |
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|
151 |
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"baseball glove": 35,
|
152 |
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"bear": 21,
|
153 |
+
"bed": 59,
|
154 |
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"bench": 13,
|
155 |
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"bicycle": 1,
|
156 |
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"bird": 14,
|
157 |
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"boat": 8,
|
158 |
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"book": 73,
|
159 |
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|
160 |
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"bowl": 45,
|
161 |
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"broccoli": 50,
|
162 |
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"bus": 5,
|
163 |
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"cake": 55,
|
164 |
+
"car": 2,
|
165 |
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"carrot": 51,
|
166 |
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"cat": 15,
|
167 |
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"cell phone": 67,
|
168 |
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"chair": 56,
|
169 |
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"clock": 74,
|
170 |
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"cow": 19,
|
171 |
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"cup": 41,
|
172 |
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|
173 |
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"dog": 16,
|
174 |
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"donut": 54,
|
175 |
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"elephant": 20,
|
176 |
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|
177 |
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"fork": 42,
|
178 |
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"frisbee": 29,
|
179 |
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"giraffe": 23,
|
180 |
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|
181 |
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"handbag": 26,
|
182 |
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"horse": 17,
|
183 |
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"hot dog": 52,
|
184 |
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"keyboard": 66,
|
185 |
+
"kite": 33,
|
186 |
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"knife": 43,
|
187 |
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"laptop": 63,
|
188 |
+
"microwave": 68,
|
189 |
+
"motorbike": 3,
|
190 |
+
"mouse": 64,
|
191 |
+
"orange": 49,
|
192 |
+
"oven": 69,
|
193 |
+
"parking meter": 12,
|
194 |
+
"person": 0,
|
195 |
+
"pizza": 53,
|
196 |
+
"pottedplant": 58,
|
197 |
+
"refrigerator": 72,
|
198 |
+
"remote": 65,
|
199 |
+
"sandwich": 48,
|
200 |
+
"scissors": 76,
|
201 |
+
"sheep": 18,
|
202 |
+
"sink": 71,
|
203 |
+
"skateboard": 36,
|
204 |
+
"skis": 30,
|
205 |
+
"snowboard": 31,
|
206 |
+
"sofa": 57,
|
207 |
+
"spoon": 44,
|
208 |
+
"sports ball": 32,
|
209 |
+
"stop sign": 11,
|
210 |
+
"suitcase": 28,
|
211 |
+
"surfboard": 37,
|
212 |
+
"teddy bear": 77,
|
213 |
+
"tennis racket": 38,
|
214 |
+
"tie": 27,
|
215 |
+
"toaster": 70,
|
216 |
+
"toilet": 61,
|
217 |
+
"toothbrush": 79,
|
218 |
+
"traffic light": 9,
|
219 |
+
"train": 6,
|
220 |
+
"truck": 7,
|
221 |
+
"tvmonitor": 62,
|
222 |
+
"umbrella": 25,
|
223 |
+
"vase": 75,
|
224 |
+
"wine glass": 40,
|
225 |
+
"zebra": 22
|
226 |
+
},
|
227 |
+
"label_noise_ratio": 0.5,
|
228 |
+
"layer_norm_eps": 1e-05,
|
229 |
+
"learn_initial_query": false,
|
230 |
+
"matcher_alpha": 0.25,
|
231 |
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"matcher_bbox_cost": 5.0,
|
232 |
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"matcher_class_cost": 2.0,
|
233 |
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"matcher_gamma": 2.0,
|
234 |
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"matcher_giou_cost": 2.0,
|
235 |
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"model_type": "rt_detr",
|
236 |
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"normalize_before": false,
|
237 |
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"num_denoising": 100,
|
238 |
+
"num_feature_levels": 3,
|
239 |
+
"num_queries": 300,
|
240 |
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"positional_encoding_temperature": 10000,
|
241 |
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"torch_dtype": "float32",
|
242 |
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"transformers_version": "4.42.0.dev0",
|
243 |
+
"use_focal_loss": true,
|
244 |
+
"use_pretrained_backbone": true,
|
245 |
+
"use_timm_backbone": true,
|
246 |
+
"weight_loss_bbox": 5.0,
|
247 |
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"weight_loss_giou": 2.0,
|
248 |
+
"weight_loss_vfl": 1.0,
|
249 |
+
"with_box_refine": true
|
250 |
+
}
|