Update model config and README

README.md

@@ -2,6 +2,152 @@
 tags:
 - image-classification
 - timm
-library_tag: timm
+library_name: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
 ---
-# Model card for fbnetv3_g.ra2_in1k
+# Model card for fbnetv3_g.ra2_in1k
+
+A FBNet-v3 image classification model. Trained on ImageNet-1k in `timm` using recipe template described below.
+
+Recipe details:
+ * RandAugment `RA2` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476).
+ * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging
+ * Step (exponential decay w/ staircase) LR schedule with warmup
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 16.6
+  - GMACs: 1.3
+  - Activations (M): 14.9
+  - Image size: train = 240 x 240, test = 288 x 288
+- **Papers:**
+  - FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining: https://arxiv.org/abs/2006.02049
+  - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
+- **Dataset:** ImageNet-1k
+- **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('fbnetv3_g.ra2_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(
+    'fbnetv3_g.ra2_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, 24, 120, 120])
+    #  torch.Size([1, 40, 60, 60])
+    #  torch.Size([1, 56, 30, 30])
+    #  torch.Size([1, 160, 15, 15])
+    #  torch.Size([1, 1728, 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(
+    'fbnetv3_g.ra2_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, 1728, 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
+@inproceedings{dai2021fbnetv3,
+  title={Fbnetv3: Joint architecture-recipe search using predictor pretraining},
+  author={Dai, Xiaoliang and Wan, Alvin and Zhang, Peizhao and Wu, Bichen and He, Zijian and Wei, Zhen and Chen, Kan and Tian, Yuandong and Yu, Matthew and Vajda, Peter and others},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={16276--16285},
+  year={2021}
+}
+```
+```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}}
+}
+```
+```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}
+}
+```

model.safetensors

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