Add config from convert_rt_detr_v2_original_pytorch_checkpoint_to_pytorch.py
Browse files- README.md +199 -0
- config.json +256 -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": null,
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"architectures": [
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"RTDetrV2ForObjectDetection"
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],
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"attention_dropout": 0.0,
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"auxiliary_loss": true,
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"backbone": null,
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"backbone_config": {
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"model_type": "rt_detr_v2_resnet",
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"out_features": [
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"stage2",
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"stage3",
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"stage4"
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],
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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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"backbone_kwargs": null,
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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": 6,
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"decoder_n_levels": 3,
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"decoder_n_points": 4,
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"decoder_offset_scale": 0.5,
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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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512,
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1024,
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2048
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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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"freeze_backbone_batch_norms": true,
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"hidden_expansion": 1.0,
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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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109 |
+
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110 |
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"43": "knife",
|
111 |
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"44": "spoon",
|
112 |
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|
113 |
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"46": "banana",
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114 |
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"47": "apple",
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115 |
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"48": "sandwich",
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116 |
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"49": "orange",
|
117 |
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"50": "broccoli",
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118 |
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"51": "carrot",
|
119 |
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"52": "hot dog",
|
120 |
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"53": "pizza",
|
121 |
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"54": "donut",
|
122 |
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"55": "cake",
|
123 |
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"56": "chair",
|
124 |
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"57": "sofa",
|
125 |
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"58": "pottedplant",
|
126 |
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"59": "bed",
|
127 |
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"60": "diningtable",
|
128 |
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"61": "toilet",
|
129 |
+
"62": "tvmonitor",
|
130 |
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"63": "laptop",
|
131 |
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"64": "mouse",
|
132 |
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"65": "remote",
|
133 |
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"66": "keyboard",
|
134 |
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"67": "cell phone",
|
135 |
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"68": "microwave",
|
136 |
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"69": "oven",
|
137 |
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"70": "toaster",
|
138 |
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"71": "sink",
|
139 |
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"72": "refrigerator",
|
140 |
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"73": "book",
|
141 |
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"74": "clock",
|
142 |
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"75": "vase",
|
143 |
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"76": "scissors",
|
144 |
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"77": "teddy bear",
|
145 |
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"78": "hair drier",
|
146 |
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"79": "toothbrush"
|
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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157 |
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158 |
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159 |
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160 |
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161 |
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162 |
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163 |
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164 |
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165 |
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167 |
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168 |
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169 |
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171 |
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172 |
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173 |
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174 |
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175 |
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177 |
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178 |
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179 |
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180 |
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181 |
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182 |
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183 |
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184 |
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186 |
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187 |
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188 |
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189 |
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190 |
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191 |
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192 |
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193 |
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194 |
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195 |
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196 |
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197 |
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198 |
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199 |
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200 |
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201 |
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202 |
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|
203 |
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204 |
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205 |
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206 |
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207 |
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208 |
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209 |
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|
210 |
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|
211 |
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212 |
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213 |
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|
214 |
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215 |
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216 |
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|
217 |
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|
218 |
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|
219 |
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|
220 |
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"tie": 27,
|
221 |
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|
222 |
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|
223 |
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|
224 |
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|
225 |
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|
226 |
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|
227 |
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|
228 |
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|
229 |
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|
230 |
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|
231 |
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|
232 |
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},
|
233 |
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|
234 |
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|
235 |
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|
236 |
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239 |
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241 |
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|
242 |
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|
243 |
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244 |
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|
245 |
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|
246 |
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|
247 |
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|
248 |
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|
249 |
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|
250 |
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|
251 |
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|
252 |
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|
253 |
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|
254 |
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|
255 |
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|
256 |
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}
|