commit files to HF hub

.gitattributes

@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+images/balcony.jpg filter=lfs diff=lfs merge=lfs -text
+images/bathroom.jpg filter=lfs diff=lfs merge=lfs -text
+images/bedroom.jpg filter=lfs diff=lfs merge=lfs -text
+images/corridor.jpg filter=lfs diff=lfs merge=lfs -text
+images/dining_room.jpg filter=lfs diff=lfs merge=lfs -text
+images/empty_room.jpg filter=lfs diff=lfs merge=lfs -text
+images/garage.jpg filter=lfs diff=lfs merge=lfs -text
+images/kitchen.jpg filter=lfs diff=lfs merge=lfs -text
+images/living_room.jpg filter=lfs diff=lfs merge=lfs -text
+images/office.jpg filter=lfs diff=lfs merge=lfs -text
+images/terrace.jpg filter=lfs diff=lfs merge=lfs -text
+images/yard.jpg filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags:
+- image-classification
+- pytorch
+- huggingpics
+metrics:
+- accuracy
+
+model-index:
+- name: Label-Room
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    metrics:
+      - name: Accuracy
+        type: accuracy
+        value: 0.7336244583129883
+---
+
+# Label-Room
+
+Test on room classification with 12 Labels : kitchen, bathroom, living room, dining room, bedroom, empty room, balcony, terrace, yard, garage, office, corridor."
+Autogenerated by HuggingPics🤗🖼️
+
+Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
+
+Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
+
+
+## Example Images

config.json

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+{
+  "_name_or_path": "google/vit-base-patch16-224-in21k",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "balcony",
+    "1": "bathroom",
+    "10": "terrace",
+    "11": "yard",
+    "2": "bedroom",
+    "3": "corridor",
+    "4": "dining room",
+    "5": "empty room",
+    "6": "garage",
+    "7": "kitchen",
+    "8": "living room",
+    "9": "office"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "balcony": "0",
+    "bathroom": "1",
+    "bedroom": "2",
+    "corridor": "3",
+    "dining room": "4",
+    "empty room": "5",
+    "garage": "6",
+    "kitchen": "7",
+    "living room": "8",
+    "office": "9",
+    "terrace": "10",
+    "yard": "11"
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2"
+}

model.safetensors

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+size 343254736

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTFeatureExtractor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

runs/events.out.tfevents.1701446946.4adc06e0cac0.163.0

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