commit files to HF hub

.gitattributes

@@ -32,3 +32,8 @@ 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/Desktop_computer.jpg filter=lfs diff=lfs merge=lfs -text
+images/Laptop.jpg filter=lfs diff=lfs merge=lfs -text
+images/Samsung_Galaxy.jpg filter=lfs diff=lfs merge=lfs -text
+images/Television.jpg filter=lfs diff=lfs merge=lfs -text
+images/iPhone.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,52 @@
 ---
-license: openrail
+tags:
+- image-classification
+- pytorch
+- huggingpics
+metrics:
+- accuracy
+
+model-index:
+- name: prd-image-search
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    metrics:
+      - name: Accuracy
+        type: accuracy
+        value: 0.7757009267807007
 ---
+
+# prd-image-search
+
+
+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
+
+
+#### Desktop computer
+
+![Desktop computer](images/Desktop_computer.jpg)
+
+#### Laptop
+
+![Laptop](images/Laptop.jpg)
+
+#### Samsung Galaxy
+
+![Samsung Galaxy](images/Samsung_Galaxy.jpg)
+
+#### Television
+
+![Television](images/Television.jpg)
+
+#### iPhone
+
+![iPhone](images/iPhone.jpg)

config.json

@@ -0,0 +1,38 @@
+{
+  "_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": "Desktop computer",
+    "1": "Laptop",
+    "2": "Samsung Galaxy",
+    "3": "Television",
+    "4": "iPhone"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Desktop computer": "0",
+    "Laptop": "1",
+    "Samsung Galaxy": "2",
+    "Television": "3",
+    "iPhone": "4"
+  },
+  "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": "4.25.1"
+}

preprocessor_config.json

@@ -0,0 +1,22 @@
+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

pytorch_model.bin

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+size 343275181

runs/events.out.tfevents.1670574593.da0d46ad4bd1

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