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LSDIR

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  1. build_dataset.ipynb +384 -0
build_dataset.ipynb ADDED
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+ {
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+ "cells": [
3
+ {
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+ "cell_type": "code",
5
+ "execution_count": 1,
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+ "id": "b74163c1-4b23-4c40-9afd-f963e4a1377d",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "from glob import glob\n",
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+ "from datasets import Dataset, Features, Image, Value\n",
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+ "from PIL import Image as PILImage\n",
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+ "from torchvision.transforms import CenterCrop, Resize\n",
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+ "import matplotlib.pyplot as plt\n",
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+ "import numpy as np\n",
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+ "import torch"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "id": "912bc52b-2c88-421a-9d3d-d85c5a0f2a47",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "LSDIR_paths = Dataset.from_dict({\"path\":glob(\"LSDIR/*/*.png\")})"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "id": "e4ffa17d-3a4e-4c84-bf1b-905001deff29",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "lanczos = PILImage.Resampling.LANCZOS\n",
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+ "def load_image(sample):\n",
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+ " image_path = sample['path']\n",
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+ " image = PILImage.open(image_path)\n",
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+ " w = image.width\n",
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+ " h = image.height\n",
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+ " aspect = w/h\n",
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+ " if aspect <= 9/16:\n",
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+ " if w<756 or h<1344:\n",
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+ " image = CenterCrop((960,540))(Resize(540,lanczos)(image))\n",
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+ " else:\n",
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+ " image = CenterCrop((1344,756))(Resize(756,lanczos)(image))\n",
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+ " elif aspect <= 2/3:\n",
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+ " if w<816 or h<1224:\n",
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+ " image = CenterCrop((810,540))(Resize(540,lanczos)(image))\n",
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+ " else:\n",
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+ " image = CenterCrop((1224,816))(Resize(816,lanczos)(image))\n",
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+ " elif aspect >= 16/9:\n",
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+ " if w<1344 or h<756:\n",
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+ " image = CenterCrop((540,960))(Resize(540,lanczos)(image))\n",
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+ " else:\n",
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+ " image = CenterCrop((756,1344))(Resize(756,lanczos)(image))\n",
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+ " elif aspect >= 8/5:\n",
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+ " if w<1248 or h<780:\n",
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+ " image = CenterCrop((540,864))(Resize(540,lanczos)(image))\n",
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+ " else:\n",
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+ " image = CenterCrop((780,1248))(Resize(780,lanczos)(image))\n",
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+ " elif aspect >= 4/3:\n",
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+ " if w<1152 or h<864:\n",
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+ " image = CenterCrop((540,720))(Resize(540,lanczos)(image))\n",
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+ " else:\n",
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+ " image = CenterCrop((864,1152))(Resize(864,lanczos)(image))\n",
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+ " else:\n",
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+ " if w<1008 or h<1008:\n",
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+ " image = CenterCrop((540,540))(Resize(540,lanczos)(image))\n",
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+ " else:\n",
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+ " image = CenterCrop((1008,1008))(Resize(1008,lanczos)(image))\n",
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+ " return {\n",
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+ " \"image\": image,\n",
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+ " \"w\":image.width,\n",
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+ " \"h\":image.height,\n",
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+ " \"mode\":image.mode,\n",
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+ " \"aspect\":image.width/image.height,\n",
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+ " \"n_pixels\":image.width*image.height}"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "id": "115873bf-a6ba-4a9d-a4c1-c2507b558e39",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "c0f5cfc20f7c4c4e88857d1bbefa1c84",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "Map (num_proc=24): 0%| | 0/84991 [00:00<?, ? examples/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "LSDIR = LSDIR_paths.map(load_image, writer_batch_size=1000, num_proc=24)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 5,
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+ "id": "0a37bca8-fca2-4faf-9a1d-dec46bf8f548",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "True"
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+ ]
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+ },
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+ "execution_count": 5,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "all(mode == 'RGB' for mode in LSDIR['mode'])"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 6,
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+ "id": "cede2255-099d-416b-b359-4eddb3a641c6",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "array([0.5625 , 0.66666667, 1. , 1.33333333, 1.6 ,\n",
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+ " 1.77777778])"
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+ ]
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+ },
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+ "execution_count": 6,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "np.unique(LSDIR['aspect'])"
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+ ]
149
+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 7,
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+ "id": "492b99a5-72bb-4356-acdb-99d7e7f3973a",
154
+ "metadata": {},
155
+ "outputs": [
156
+ {
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+ "data": {
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+ "image/png": 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",
159
+ "text/plain": [
160
+ "<Figure size 640x480 with 1 Axes>"
161
+ ]
162
+ },
163
+ "metadata": {},
164
+ "output_type": "display_data"
165
+ }
166
+ ],
167
+ "source": [
168
+ "plt.hist(LSDIR['aspect'],bins=12, range=(0.5,2));"
169
+ ]
170
+ },
171
+ {
172
+ "cell_type": "code",
173
+ "execution_count": 8,
174
+ "id": "0ee68902-8cbf-404f-9141-85230b8ef0e6",
175
+ "metadata": {},
176
+ "outputs": [
177
+ {
178
+ "data": {
179
+ "image/png": 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",
180
+ "text/plain": [
181
+ "<Figure size 640x480 with 1 Axes>"
182
+ ]
183
+ },
184
+ "metadata": {},
185
+ "output_type": "display_data"
186
+ }
187
+ ],
188
+ "source": [
189
+ "plt.hist(LSDIR['h'],bins=12,alpha=0.7);\n",
190
+ "plt.hist(LSDIR['w'],bins=12,alpha=0.7);"
191
+ ]
192
+ },
193
+ {
194
+ "cell_type": "code",
195
+ "execution_count": null,
196
+ "id": "90f2874d-3f0d-4ad4-bcc1-3766fb87815a",
197
+ "metadata": {},
198
+ "outputs": [],
199
+ "source": [
200
+ "LSDIR.push_to_hub(\"danjacobellis/LSDIR\",split=\"train\") "
201
+ ]
202
+ },
203
+ {
204
+ "cell_type": "code",
205
+ "execution_count": 2,
206
+ "id": "b442f513-e137-4800-a782-2256db2c784f",
207
+ "metadata": {},
208
+ "outputs": [],
209
+ "source": [
210
+ "LSDIR_paths = Dataset.from_dict({\"path\":glob(\"LSDIR_val/val1/HR/val/*.png\")})"
211
+ ]
212
+ },
213
+ {
214
+ "cell_type": "code",
215
+ "execution_count": 3,
216
+ "id": "249ad020-35be-40c6-b6f9-f601b48eebae",
217
+ "metadata": {},
218
+ "outputs": [],
219
+ "source": [
220
+ "def load_image_val(sample):\n",
221
+ " image_path = sample['path']\n",
222
+ " image = PILImage.open(image_path)\n",
223
+ " return {\n",
224
+ " \"image\": image,\n",
225
+ " \"w\":image.width,\n",
226
+ " \"h\":image.height,\n",
227
+ " \"mode\":image.mode,\n",
228
+ " \"aspect\":image.width/image.height,\n",
229
+ " \"n_pixels\":image.width*image.height}"
230
+ ]
231
+ },
232
+ {
233
+ "cell_type": "code",
234
+ "execution_count": 4,
235
+ "id": "4ad4f210-1290-40dd-850b-de0ed8f8c05f",
236
+ "metadata": {},
237
+ "outputs": [
238
+ {
239
+ "data": {
240
+ "application/vnd.jupyter.widget-view+json": {
241
+ "model_id": "6d89cdec3c664a6c84e100e4828d6959",
242
+ "version_major": 2,
243
+ "version_minor": 0
244
+ },
245
+ "text/plain": [
246
+ "Map (num_proc=24): 0%| | 0/250 [00:00<?, ? examples/s]"
247
+ ]
248
+ },
249
+ "metadata": {},
250
+ "output_type": "display_data"
251
+ }
252
+ ],
253
+ "source": [
254
+ "LSDIR = LSDIR_paths.map(load_image_val, writer_batch_size=1000, num_proc=24)"
255
+ ]
256
+ },
257
+ {
258
+ "cell_type": "code",
259
+ "execution_count": 7,
260
+ "id": "ad72c75d-5cae-4496-b055-c345d6d73456",
261
+ "metadata": {},
262
+ "outputs": [
263
+ {
264
+ "data": {
265
+ "application/vnd.jupyter.widget-view+json": {
266
+ "model_id": "b6b4e45fef7840bf80fd4421a50767b1",
267
+ "version_major": 2,
268
+ "version_minor": 0
269
+ },
270
+ "text/plain": [
271
+ "Uploading the dataset shards: 0%| | 0/2 [00:00<?, ?it/s]"
272
+ ]
273
+ },
274
+ "metadata": {},
275
+ "output_type": "display_data"
276
+ },
277
+ {
278
+ "data": {
279
+ "application/vnd.jupyter.widget-view+json": {
280
+ "model_id": "0b8e27315de74f57a04f89ccb260756a",
281
+ "version_major": 2,
282
+ "version_minor": 0
283
+ },
284
+ "text/plain": [
285
+ "Map: 0%| | 0/125 [00:00<?, ? examples/s]"
286
+ ]
287
+ },
288
+ "metadata": {},
289
+ "output_type": "display_data"
290
+ },
291
+ {
292
+ "data": {
293
+ "application/vnd.jupyter.widget-view+json": {
294
+ "model_id": "eb897d1914c34f429058e633167ecfe7",
295
+ "version_major": 2,
296
+ "version_minor": 0
297
+ },
298
+ "text/plain": [
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+ ]
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+ "metadata": {},
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+ },
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+ {
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+ "data": {
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+ ]
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+ "metadata": {},
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+ },
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+ {
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+ "data": {
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+ "text/plain": [
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+ "CommitInfo(commit_url='https://huggingface.co/datasets/danjacobellis/LSDIR/commit/fa63056ce9e7b118d5b29b3249254d4e8142cdad', commit_message='Upload dataset', commit_description='', oid='fa63056ce9e7b118d5b29b3249254d4e8142cdad', pr_url=None, pr_revision=None, pr_num=None)"
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+ },
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+ "execution_count": 7,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "LSDIR.push_to_hub(\"danjacobellis/LSDIR\",split=\"validation\")"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "language": "python",
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+ "name": "python3"
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.10.12"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 5
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+ }