# Model card for darknetaa53.c2ns_in1k

A DarkNet image classification model. Trained on ImageNet-1k in `timm` using recipe template described below.

Recipe details:
 * Based on [ResNet Strikes Back](https://arxiv.org/abs/2110.00476) `C` recipes w/o repeat-aug and stronger mixup
 * SGD (w/ Nesterov) optimizer and AGC (adaptive gradient clipping)
 * No stochastic depth used in this `ns` variation of the recipe
 * Cosine LR schedule with warmup


## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 36.0
  - GMACs: 8.0
  - Activations (M): 12.4
  - Image size: train = 256 x 256, test = 288 x 288
- **Papers:**
  - YOLOv3: An Incremental Improvement: https://arxiv.org/abs/1804.02767
  - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
- **Original:** https://github.com/huggingface/pytorch-image-models

## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('darknetaa53.c2ns_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```

### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'darknetaa53.c2ns_in1k',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 32, 256, 256])
    #  torch.Size([1, 64, 128, 128])
    #  torch.Size([1, 128, 64, 64])
    #  torch.Size([1, 256, 32, 32])
    #  torch.Size([1, 512, 16, 16])
    #  torch.Size([1, 1024, 8, 8])

    print(o.shape)
```

### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'darknetaa53.c2ns_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1024, 8, 8) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```

## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).

## Citation
```bibtex
@article{Redmon2018YOLOv3AI,
  title={YOLOv3: An Incremental Improvement},
  author={Joseph Redmon and Ali Farhadi},
  journal={ArXiv},
  year={2018},
  volume={abs/1804.02767}
}
```
```bibtex
@inproceedings{wightman2021resnet,
  title={ResNet strikes back: An improved training procedure in timm},
  author={Wightman, Ross and Touvron, Hugo and Jegou, Herve},
  booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future}
}
```
```bibtex
@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```

{"license": "apache-2.0", "library_name": "timm", "tags": ["image-classification", "timm"]}
timm pytorch safetensors image-classification arxiv:2110.00476 arxiv:1804.02767 license:apache-2.0 region:us
TAGS
#timm #pytorch #safetensors #image-classification #arxiv-2110.00476 #arxiv-1804.02767 #license-apache-2.0 #region-us 

# Model card for darknetaa53.c2ns_in1k

A DarkNet image classification model. Trained on ImageNet-1k in 'timm' using recipe template described below.

Recipe details:
 * Based on ResNet Strikes Back 'C' recipes w/o repeat-aug and stronger mixup
 * SGD (w/ Nesterov) optimizer and AGC (adaptive gradient clipping)
 * No stochastic depth used in this 'ns' variation of the recipe
 * Cosine LR schedule with warmup


## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 36.0
  - GMACs: 8.0
  - Activations (M): 12.4
  - Image size: train = 256 x 256, test = 288 x 288
- Papers:
  - YOLOv3: An Incremental Improvement: URL
  - ResNet strikes back: An improved training procedure in timm: URL
- Original: URL

## Model Usage
### Image Classification


### Feature Map Extraction


### Image Embeddings


## Model Comparison
Explore the dataset and runtime metrics of this model in timm model results.


# Model card for darknetaa53.c2ns_in1k

A DarkNet image classification model. Trained on ImageNet-1k in 'timm' using recipe template described below.

Recipe details:
 * Based on ResNet Strikes Back 'C' recipes w/o repeat-aug and stronger mixup
 * SGD (w/ Nesterov) optimizer and AGC (adaptive gradient clipping)
 * No stochastic depth used in this 'ns' variation of the recipe
 * Cosine LR schedule with warmup ## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 36.0
  - GMACs: 8.0
  - Activations (M): 12.4
  - Image size: train = 256 x 256, test = 288 x 288
- Papers:
  - YOLOv3: An Incremental Improvement: URL
  - ResNet strikes back: An improved training procedure in timm: URL
- Original: URL ## Model Usage ### Image Classification ### Feature Map Extraction ### Image Embeddings ## Model Comparison
Explore the dataset and runtime metrics of this model in timm model results.
TAGS
#timm #pytorch #safetensors #image-classification #arxiv-2110.00476 #arxiv-1804.02767 #license-apache-2.0 #region-us 
 # Model card for darknetaa53.c2ns_in1k

A DarkNet image classification model. Trained on ImageNet-1k in 'timm' using recipe template described below.

Recipe details:
 * Based on ResNet Strikes Back 'C' recipes w/o repeat-aug and stronger mixup
 * SGD (w/ Nesterov) optimizer and AGC (adaptive gradient clipping)
 * No stochastic depth used in this 'ns' variation of the recipe
 * Cosine LR schedule with warmup ## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 36.0
  - GMACs: 8.0
  - Activations (M): 12.4
  - Image size: train = 256 x 256, test = 288 x 288
- Papers:
  - YOLOv3: An Incremental Improvement: URL
  - ResNet strikes back: An improved training procedure in timm: URL
- Original: URL ## Model Usage ### Image Classification ### Feature Map Extraction ### Image Embeddings ## Model Comparison
Explore the dataset and runtime metrics of this model in timm model results.
TAGS
#timm #pytorch #safetensors #image-classification #arxiv-2110.00476 #arxiv-1804.02767 #license-apache-2.0 #region-us 
# Model card for darknetaa53.c2ns_in1k

A DarkNet image classification model. Trained on ImageNet-1k in 'timm' using recipe template described below.

Recipe details:
 * Based on ResNet Strikes Back 'C' recipes w/o repeat-aug and stronger mixup
 * SGD (w/ Nesterov) optimizer and AGC (adaptive gradient clipping)
 * No stochastic depth used in this 'ns' variation of the recipe
 * Cosine LR schedule with warmup## Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
  - Params (M): 36.0
  - GMACs: 8.0
  - Activations (M): 12.4
  - Image size: train = 256 x 256, test = 288 x 288
- Papers:
  - YOLOv3: An Incremental Improvement: URL
  - ResNet strikes back: An improved training procedure in timm: URL
- Original: URL## Model Usage### Image Classification### Feature Map Extraction### Image Embeddings## Model Comparison
Explore the dataset and runtime metrics of this model in timm model results.