Add model

README.md

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+---
+tags:
+- image-classification
+- timm
+library_name: timm
+license: apache-2.0
+datasets:
+- imagenet-1k
+---
+# Model card for mobilenetv4_conv_medium.e500_r256_in1k
+
+A MobileNet-V4 image classification model. Trained on ImageNet-1k by Ross Wightman.
+
+Trained with `timm` scripts using hyper-parameters (mostly) similar to those in the paper.
+
+NOTE: So far, these are the only known MNV4 weights. Official weights for Tensorflow models are unreleased.
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 9.7
+  - GMACs: 1.1
+  - Activations (M): 7.6
+  - Image size: train = 256 x 256, test = 320 x 320
+- **Dataset:** ImageNet-1k
+- **Papers:**
+  - MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518
+- **Original:** https://github.com/tensorflow/models/tree/master/official/vision
+
+## 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('mobilenetv4_conv_medium.e500_r256_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(
+    'mobilenetv4_conv_medium.e500_r256_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, 128, 128])
+    #  torch.Size([1, 48, 64, 64])
+    #  torch.Size([1, 80, 32, 32])
+    #  torch.Size([1, 160, 16, 16])
+    #  torch.Size([1, 960, 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(
+    'mobilenetv4_conv_medium.e500_r256_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, 960, 8, 8) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+### By Top-1
+
+|model                                      |top1  |top1_err|top5  |top5_err|param_count|img_size|
+|-------------------------------------------|------|--------|------|--------|-----------|--------|
+|mobilenetv4_conv_large.e500_r256_in1k      |82.674|17.326  |96.31 |3.69    |32.59      |320     |
+|mobilenetv4_conv_large.e500_r256_in1k      |81.862|18.138  |95.69 |4.31    |32.59      |256     |
+|mobilenetv4_hybrid_medium.e500_r224_in1k   |81.276|18.724  |95.742|4.258   |11.07      |256     |
+|mobilenetv4_conv_medium.e500_r256_in1k     |80.858|19.142  |95.768|4.232   |9.72       |320     |
+|mobilenetv4_hybrid_medium.e500_r224_in1k   |80.442|19.558  |95.38 |4.62    |11.07      |224     |
+|mobilenetv4_conv_blur_medium.e500_r224_in1k|80.142|19.858  |95.298|4.702   |9.72       |256     |
+|mobilenetv4_conv_medium.e500_r256_in1k     |79.928|20.072  |95.184|4.816   |9.72       |256     |
+|mobilenetv4_conv_medium.e500_r224_in1k     |79.808|20.192  |95.186|4.814   |9.72       |256     |
+|mobilenetv4_conv_blur_medium.e500_r224_in1k|79.438|20.562  |94.932|5.068   |9.72       |224     |
+|mobilenetv4_conv_medium.e500_r224_in1k     |79.094|20.906  |94.77 |5.23    |9.72       |224     |
+|mobilenetv4_conv_small.e1200_r224_in1k     |74.292|25.708  |92.116|7.884   |3.77       |256     |
+|mobilenetv4_conv_small.e1200_r224_in1k     |73.454|26.546  |91.34 |8.66    |3.77       |224     |

config.json

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+{
+  "architecture": "mobilenetv4_conv_medium",
+  "num_classes": 1000,
+  "num_features": 960,
+  "pretrained_cfg": {
+    "tag": "e500_r256_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      256,
+      256
+    ],
+    "test_input_size": [
+      3,
+      320,
+      320
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 0.95,
+    "test_crop_pct": 1.0,
+    "crop_mode": "center",
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "num_classes": 1000,
+    "pool_size": [
+      8,
+      8
+    ],
+    "first_conv": "conv_stem",
+    "classifier": "classifier"
+  }
+}

model.safetensors

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pytorch_model.bin

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